4.8.2 Perceptual Grouping, Saliency, General Systems

Kelly, R.E., McConnell, P.R.M., Mildenberger, S.J.,
The Gestalt Photomapping System,
PhEngRS(43), No. 11, November 1977, pp. 1407-1417. BibRef 7711

Smith, B.J.[Beverly J.],
Perception of Organization in a Random Stimulus,
CVGIP(31), No. 2, August 1985, pp. 242-247.
Elsevier DOI BibRef 8508
Earlier: ICPR84(512-514). Human perception test of organization. BibRef

Lowe, D.G.,
Perceptual Organization and Visual Recognition,
Boston: KluwerAcademic Publishers, June 1985. BibRef 8506 Ph.D.Thesis (CS). ISBN 0-89838-172-X. Grouping, Perceptual. Grouping, Models. Recognition, Model Based.
See also Recovery of Three-Dimensional Structure from Image Curves, The.
Springer DOI BibRef

Lowe, D.G.[David G.], and Binford, T.O.[Thomas O.],
The Perceptual Organization of Visual Images: Segmentation as a Basis for Recognition,
DARPA83(203-209). BibRef 8300
And:
Perceptual Organization as a Basis for Visual Recognition,
AAAI-83(255-260). BibRef
Earlier:
Segmentation and Aggregation: An Approach to Figure-Ground Phenomena,
DARPA82(168-178), BibRef RCV87(282-292). Figure-Ground separation. Bottom up grouping is a prerequisite for recognition. This breaks into 3 types of grouping: segmentation, 3D interpretation, descriptions of objects. BibRef

Lowe, D.G.,
Integrated Treatment of Matching and Measurement Errors for Robust Model-Based Motion Tracking,
ICCV90(436-440).
IEEE DOI BibRef 9000

Dickson, W.[Will],
Feature Grouping in a Hierarchical Probabilistic Network,
IVC(9), No. 1, February 1991, pp. 51-57.
Elsevier DOI BibRef 9102
Earlier: BMVC90(xx-yy).
PDF File. 9009
BibRef

Mohan, R., and Nevatia, R.,
Perceptual Organization for Scene Segmentation and Description,
PAMI(14), No. 6, June 1992, pp. 616-635.
IEEE DOI BibRef 9206 USC Computer Vision BibRef
Earlier:
Segmentation and Description Based on Perceptual Organization,
CVPR89(333-341).
IEEE DOI BibRef
And:
Perceptual Organization for Segmentation and Description,
DARPA89(415-424). Segmentation, Grouping. Groupings of line features are located by co-curvilinearity and symmetry to find curves, symmetries and ribbons. These combine to give 2-D shapes and object surfaces. Combination uses a Hopfield network.
See also Using Perceptual Organization to Extract 3-D Structures. BibRef

Mohan, R.,
Perceptual Organization for Computer Vision,
USC_IRISTR-254, August 1989, BibRef 8908 Ph.D.Thesis (CS). Thesis with perceptual organization for segmentation and matching applications. BibRef

Ahuja, N.[Narendra], Tuceryan, M.[Mihran],
Extraction of Early Perceptual Structure in Dot Patterns: Integrating Region, Boundary, and Component Gestalt,
CVGIP(48), No. 3, December 1989, pp. 304-356.
Elsevier DOI BibRef 8912
Earlier: A2, A1:
Perceptual Segmentation of Nonhomogeneous Dot Patterns,
CVPR83(47-52). Relaxation. Group dots into the perceptual groups using multiple constraints. BibRef

Tuceryan, M., Jain, A.K., Ahuja, N.,
Supervised classification of early perceptual structure in dot patterns,
ICPR92(II:88-91).
IEEE DOI 9208
BibRef

Mjolsness, E., Gindi, G., and Anandan, P.,
Optimization in Model Matching and Perceptual Organization,
NeurComp(1), 1989, pp. 218-229. BibRef 8900
And:
Optimization in Model Matching and Perceptual Organization: A First Look,
YaleCS, YaleU/DCS/RR-634, June 1988. Hopfield network. BibRef

Sarkar, S., Boyer, K.L.,
Perceptual Organization in Computer Vision: A Review and a Proposal for a Classificatory Structure,
SMC(23), No. 2, 1993, pp. 382-399. BibRef 9300

Sarkar, S., and Boyer, K.L.,
Integration, Inference, and Management of Spatial Information Using Bayesian Networks: Perceptual Organization,
PAMI(15), No. 3, March 1993, pp. 256-274.
IEEE DOI Bayes Nets. BibRef 9303
Earlier:
Perceptual Organization Using Bayesian Networks,
CVPR92(251-256).
IEEE DOI Integrate a number of different systems. BibRef

Sarkar, S., Boyer, K.L.,
Using Perceptual Inference Networks To Manage Vision Processes,
CVIU(62), No. 1, July 1995, pp. 27-46.
DOI Link BibRef 9507
Earlier: ICPR94(A:808-810).
IEEE DOI BibRef

Sarkar, S., and Boyer, K.L.,
Computing Perceptual Organization in Computer Vision,
World Scientific1994. (ISBN: 981-02-1832-X). 232pp. BibRef 9400 Book Code, Perceptual Grouping. Code:
HTML Version. Based on Sarkar's thesis. Derive a framework for perceptual organization at various levels. lower levels feed higher levels. Does not get to the recognition level. BibRef

Sarkar, S., Boyer, K.L.,
Automated Design of Bayesian Perceptual Inference Networks,
CVPR94(98-103).
IEEE DOI BibRef 9400

Sarkar, S., Boyer, K.L.,
A Computational Structure for Preattentive Perceptual Organization: Graphical Enumeration and Voting Methods,
SMC(24), 1994, pp. 246-267. BibRef 9400

Sarkar, S., Boyer, K.L.,
Computing Perceptual Organization Using Voting Methods and Graphical Enumeration,
ICPR92(I:263-267).
IEEE DOI BibRef 9200

Pun, T.[Thierry],
Electromagnetic Models for Perceptual Grouping,
AMV Strategies921992, pp. 129-149. BibRef 9200

Saund, E.[Eric],
Putting Knowledge into a Visual Shape Representation,
AI(54), No. 1-2, March 1992, pp. 71-119.
Elsevier DOI BibRef 9203
Earlier:
Representation and the Dimensions of Shape Deformation,
ICCV90(684-689).
IEEE DOI BibRef
And:
The Role of Knowledge in Visual Shape Representation,
MIT AI-TR-1092, October 1988.
WWW Link. BibRef

Chen, L.H.,
A New Approach for Feature Point Classification, Aggregation, and Description,
PRAI(6), 1992, pp. 849-871. BibRef 9200

von der Malsburg, C.[Christoph], and Schneider, W.,
A Neural Cocktail-Party Processor,
BioCyber(54), 1986, pp. 29-40. BibRef 8600

von der Malsburg, C.[Christoph], and Buhmann, J.M.,
Sensory Segmentation with Coupled Neural Oscillators,
BioCyber(67), 1993, pp. 233-242. BibRef 9300

Lebegue, X., Aggarwal, J.K.,
Significant Line Segments for an Indoor Mobile Robot,
RA(9), 1993, pp. 801-815. BibRef 9300
And:
Detecting 3D Parallel Lines for Perceptual Organization,
ECCV92(720-724).
Springer DOI BibRef

Denasi, S., Quaglia, G., and Rinaudi, D.,
The Use of Perceptual Organization in the Prediction of Geometric Structures,
PRL(13), No. 7, 1991, pp. 529-539. BibRef 9100

Shashua, A., and Ullman, S.,
Structural Saliency: The Detection of Globally Salient Structures Using a Locally Connected Network,
ICCV88(321-327).
IEEE DOI BibRef 8800
And: A2, A1: MIT AI Memo-1061, July 1988. BibRef

Shashua, A., and Ullman, S.,
Grouping Contours by Iterated Pairing Network,
Neural Info(3), 1991, pp. 335-341, BibRef 9100

Borra, S.[Sudhir], Sarkar, S.[Sudeep],
A Framework for Performance Characterization of Intermediate Level Grouping Modules,
PAMI(19), No. 11, November 1997, pp. 1306-1312.
IEEE DOI Code and images available:
HTML Version. 9712
Compare (in order of ranking): Jacobs:
See also Robust and Efficient Detection of Salient Convex Groups. Sarkar-Boyer:
See also Integration, Inference, and Management of Spatial Information Using Bayesian Networks: Perceptual Organization. Etemadi:
See also Low-Level Grouping of Straight Line Segments. BibRef

Feldman, J.[Jacob],
Perceptual Grouping by Selection of a Logically Minimal Model,
IJCV(55), No. 1, September 2003, pp. 5-25.
DOI Link 0307
BibRef

Feldman, J.[Jacob],
Regularity-Based Perceptual Grouping,
CompIntel(13), No. 4, November 1997, pp. 582-623. 9801
BibRef
Earlier:
Efficient Regularity-Based Grouping,
CVPR97(288-294).
IEEE DOI 9704
Grouping, general. BibRef

Feldman, J.,
Constructing perceptual categories,
CVPR92(244-250).
IEEE DOI 0403
BibRef

Amir, A., Lindenbaum, M.,
A Generic Grouping Algorithm and Its Quantitative Analysis,
PAMI(20), No. 2, February 1998, pp. 168-185.
IEEE DOI 9803
Grouping by graph clustering. Find lines and curves in noisy images. BibRef

Amir, A., Lindenbaum, M.,
Quantitative Analysis of Grouping Processes,
ECCV96(I:369-384).
Springer DOI BibRef 9600

Amir, A., Lindenbaum, M.,
Grouping-Based Nonadditive Verification,
PAMI(20), No. 2, February 1998, pp. 186-192.
IEEE DOI 9803
BibRef

Boyer, K.L.[Kim L.], Sarkar, S.[Sudeep],
Perceptual Organization in Computer Vision: Status, Challenges, and Potential,
CVIU(76), No. 1, October 1999, pp. 1-6.
DOI Link Guest Editors' Introduction. Perceptual Grouping BibRef 9910

Boyer, K.L.[Kim L.], Sarkar, S.[Sudeep],
Perceptual Organization for Artificial Vision Systems,
KluwerMarch 2000, ISBN 0-7923-7799-0
WWW Link. BibRef 0003

Foresti, G.L., Regazzoni, C.S.,
A Hierarchical Approach to Feature Extraction and Grouping,
IP(9), No. 6, June 2000, pp. 1056-1074.
IEEE DOI 0006
BibRef

Luo, J.B.[Jie-Bo], Singhal, A.[Amit],
On Measuring Low-Level Self and Relative Saliency in Photographic Images,
PRL(22), No. 2, February 2001, pp. 157-169.
Elsevier DOI 0101
BibRef
Earlier:
On Measuring Low-Level Saliency in Photographic Images,
CVPR00(I: 84-89).
IEEE DOI 0005
Seg. by Saliency BibRef

Wu, T.P.[Tai-Pang], Yeung, S.K.[Sai-Kit], Jia, J.Y.[Jia-Ya], Tang, C.K.[Chi-Keung], Medioni, G.[Gerard],
A Closed-Form Solution to Tensor Voting: Theory and Applications,
PAMI(34), No. 8, August 2012, pp. 1482-1495.
IEEE DOI 1206
Tensor Voting. Closed form solution. Exact, continuous, efficient algorithm to compute tensor for structure detection and outlier attenuation.
See also Comments on 'A Closed-Form Solution to Tensor Voting: Theory and Applications'. BibRef

Johansen, P.[Peter], Ersbøll, B.K.[Bjarne K.],
Guest Editors' Introduction,
IJCV(42), No. 1-2, April-May 2001, pp. 5-5.
DOI Link 0106
BibRef
And: JMIV(15), No. 1/2, July 2001, pp. 5-5. 0106
Perceptual grouping. Papers in both journals. BibRef

Pauli, J.[Josef], Sommer, G.[Gerald],
Perceptual organization with image formation compatibilities,
PRL(23), No. 7, May 2002, pp. 803-817.
Elsevier DOI 0203
BibRef

Zweck, J.[John], Williams, L.R.[Lance R.],
Euclidean Group Invariant Computation of Stochastic Completion Fields Using Shiftable-Twistable Functions,
JMIV(21), No. 2, September 2004, pp. 135-154.
DOI Link 0409
BibRef
Earlier: ECCV00(II: 100).
Springer DOI 0003
BibRef

Maeder, A.J.[Anthony J.],
The image importance approach to human vision based image quality characterization,
PRL(26), No. 3, February 2005, pp. 347-354.
Elsevier DOI 0501
BibRef

Maeder, A.J.[Anthony J.], Osberger, W.[Wilfried],
Automatic Identification of Perceptually Important Regions in an Image Using a Model of the Human Visual System,
ICPR98(Vol I: 701-704).
IEEE DOI Features used to find salient regions. BibRef 9800

Chen, H.T.[Hwann-Tzong], Liu, T.L.[Tyng-Luh], Fuh, C.S.[Chiou-Shann],
Tone Reproduction: A Perspective from Luminance-Driven Perceptual Grouping,
IJCV(65), No. 1-2, November 2005, pp. 73-96.
Springer DOI 0604
BibRef
Earlier: A1 and A2 only, Add A3: Chang, T.L.[Tien-Lung], CVPR05(II: 369-376).
IEEE DOI 0507
BibRef

Feldman, T.[Thomas], Younes, L.[Laurent],
Homeostatic image perception: An artificial system,
CVIU(102), No. 1, April 2006, pp. 70-80.
Elsevier DOI 0604
Image model, Visual system, Gibbs distribution, Saliency detection Complements PCA by analyzing interactions. BibRef

Parvin, B., Yang, Q.[Qing], Han, J., Chang, H., Rydberg, B., Barcellos-Hoff, M.H.,
Iterative Voting for Inference of Structural Saliency and Characterization of Subcellular Events,
IP(16), No. 3, March 2007, pp. 615-623.
IEEE DOI 0703

See also Tool for the Quantitative Spatial Analysis of Complex Cellular Systems, A. BibRef

Yang, Q.[Qing], Parvin, B., Barcellos-Hoff, M.H.,
Localization of saliency through iterative voting,
ICPR04(I: 63-66).
IEEE DOI 0409
BibRef

Hu, J.Y.[Jian-Ying], Mojsilovic, A.[Aleksandra],
High-utility pattern mining: A method for discovery of high-utility item sets,
PR(40), No. 11, November 2007, pp. 3317-3324.
Elsevier DOI 0707
High-utility item sets, Pattern mining, Partition tree BibRef

Loss, L.A.[Leandro A.], Bebis, G.N.[George N.], Nicolescu, M.[Mircea], Skurikhin, A.N.[Alexei N.],
An iterative multi-scale tensor voting scheme for perceptual grouping of natural shapes in cluttered backgrounds,
CVIU(113), No. 1, January 2009, pp. 126-149.
Elsevier DOI 0812
BibRef
Earlier:
An Automatic Framework for Figure-Ground Segmentation in Cluttered Backgrounds,
BMVC07(xx-yy).
PDF File. 0709
BibRef
Earlier:
Perceptual Grouping Based on Iterative Multi-scale Tensor Voting,
ISVC06(II: 870-881).
Springer DOI 0611
Segmentation, Boundary detection, Grouping, Object detection, Tensor voting BibRef

Loss, L.A.[Leandro A.], Bebis, G.N.[George N.], Parvin, B.[Bahram],
Iterative Tensor Voting for Perceptual Grouping of Ill-Defined Curvilinear Structures,
MedImg(30), No. 8, August 2011, pp. 1503-1513.
IEEE DOI 1108
BibRef
Earlier:
Tunable tensor voting improves grouping of membrane-bound macromolecules,
MMBIA09(72-78).
IEEE DOI 0906
BibRef

Guo, C.L.[Chen-Lei], Zhang, L.M.[Li-Ming],
A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression,
IP(19), No. 1, January 2010, pp. 185-198.
IEEE DOI 1001
BibRef

Guo, C.L.[Chen-Lei], Ma, Q.[Qi], Zhang, L.M.[Li-Ming],
Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Liu, Z.[Zhi], Xue, Y.[Yinzhu], Yan, H., Zhang, Z.Y.[Zhao-Yang],
Efficient saliency detection based on gaussian models,
IET-IPR(5), No. 2, April 2011, pp. 122-131.
DOI Link 1103
BibRef

Li, Y., Fu, K., Liu, Z., Yang, J.,
Efficient Saliency-Model-Guided Visual Co-Saliency Detection,
SPLetters(22), No. 5, May 2015, pp. 588-592.
IEEE DOI 1411
Computational modeling BibRef

Liu, Z.[Zhi], Xue, Y.[Yinzhu], Shen, L.Q.[Li-Quan], Zhang, Z.Y.[Zhao-Yang],
Nonparametric saliency detection using kernel density estimation,
ICIP10(253-256).
IEEE DOI 1009
BibRef

Song, Y.Z.[Yi-Zhe], Xiao, B.[Bai], Hall, P.M.[Peter M.], Wang, L.,
In Search of Perceptually Salient Groupings,
IP(20), No. 4, April 2011, pp. 935-947.
IEEE DOI 1103
BibRef

Xiao, B.[Bai], Song, Y.Z.[Yi-Zhe], Balika, A.[Anupriya], Hall, P.M.[Peter M.],
Structure Is a Visual Class Invariant,
SSPR08(329-338).
Springer DOI 0812
BibRef

Song, Y.Z.[Yi-Zhe], Hall, P.M.[Peter M.],
Stable Image Descriptions Using Gestalt Principles,
ISVC08(I: 318-327).
Springer DOI 0812
BibRef

Toet, A.,
Computational versus Psychophysical Bottom-Up Image Saliency: A Comparative Evaluation Study,
PAMI(33), No. 11, November 2011, pp. 2131-2146.
IEEE DOI 1110
Compare 13 models, plus Multiscale Contrast Conspicuity (MCC) metric, with Human experiments. Simple multiscale contrast model and the MCC metric both yield the largest correlation with human results. BibRef

Moreno, R.[Rodrigo], Garcia, M.A.[Miguel Angel], Puig, D.[Domenec], Pizarro, L., Burgeth, B., Weickert, J.,
On Improving the Efficiency of Tensor Voting,
PAMI(33), No. 11, November 2011, pp. 2215-2228.
IEEE DOI 1110
Introduce alternate computational formulations to reduce high computational cost.
See also Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning.
See also Edge-preserving color image denoising through tensor voting. BibRef

Li, H.L.[Hong-Liang], Ngan, K.N.[King Ngi],
A Co-Saliency Model of Image Pairs,
IP(20), No. 12, December 2011, pp. 3365-3375.
IEEE DOI 1112
combine single image saliency and multi-image saliency maps. BibRef

Luo, W.[Wang], Li, H.L.[Hong-Liang], Liu, G.H.[Guang-Hui], Ngan, K.N.[King Ngi],
Global salient information maximization for saliency detection,
SP:IC(27), No. 3, March 2012, pp. 238-248.
Elsevier DOI 1203
PCA, Information maximization, Saliency detection BibRef

Hou, X.D.[Xiao-Di], Harel, J.[Jonathan], Koch, C.[Christof],
Image Signature: Highlighting Sparse Salient Regions,
PAMI(34), No. 1, January 2012, pp. 194-201.
IEEE DOI 1112
Compute signature approximates the foreground of an image. Predict human fixation points. BibRef

Hou, X.D.[Xiao-Di], Zhang, L.Q.[Li-Qing],
Saliency Detection: A Spectral Residual Approach,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Fang, Y.M.[Yu-Ming], Lin, W.S.[Wei-Si], Lee, B.S., Lau, C.T., Chen, Z.Z.[Zhen-Zhong], Lin, C.W.,
Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude Spectrum,
MultMed(14), No. 1, January 2012, pp. 187-198.
IEEE DOI 1201
BibRef

Fang, Y.M.[Yu-Ming], Chen, Z.Z.[Zhen-Zhong], Lin, W.S.[Wei-Si], Lin, C.W.[Chia-Wen],
Saliency Detection in the Compressed Domain for Adaptive Image Retargeting,
IP(21), No. 9, September 2012, pp. 3888-3901.
IEEE DOI 1208
BibRef

Fang, Y.M.[Yu-Ming], Lin, W.S.[Wei-Si], Chen, Z.Z.[Zhen-Zhong], Tsai, C.M.[Chia-Ming], Lin, C.W.[Chia-Wen],
A Video Saliency Detection Model in Compressed Domain,
CirSysVideo(24), No. 1, January 2014, pp. 27-38.
IEEE DOI 1402
data compression BibRef

Fang, Y.M.[Yu-Ming], Wang, Z.[Zhou], Lin, W.S.[Wei-Si], Fang, Z.J.[Zhi-Jun],
Video Saliency Incorporating Spatiotemporal Cues and Uncertainty Weighting,
IP(23), No. 9, September 2014, pp. 3910-3921.
IEEE DOI 1410
statistical analysis BibRef

Dong, L.[Lu], Lin, W.S.[Wei-Si], Fang, Y.M.[Yu-Ming], Wu, S.Q.[Shi-Qian], Seah, H.S.[Hock Soon],
Saliency detection in computer rendered images based on object-level contrast,
JVCIR(25), No. 3, 2014, pp. 525-533.
Elsevier DOI 1403
BibRef
Earlier:
Detection of salient objects in computer synthesized images based on object-level contrast,
VCIP13(1-6)
IEEE DOI 1402
Graphic saliency detection. feature extraction BibRef

Fang, Y.M.[Yu-Ming], Wang, J.[Junle], Narwaria, M., Le Callet, P., Lin, W.S.[Wei-Si],
Saliency Detection for Stereoscopic Images,
IP(23), No. 6, June 2014, pp. 2625-2636.
IEEE DOI 1406
BibRef
Earlier:
Saliency detection for stereoscopic images,
VCIP13(1-6)
IEEE DOI 1402
Computational modeling. Gaussian processes BibRef

Lang, C., Liu, G., Yu, J., Yan, S.,
Saliency Detection by Multitask Sparsity Pursuit,
IP(21), No. 3, March 2012, pp. 1327-1338.
IEEE DOI 1203
BibRef

Victor, J.D.[Jonathan D.], Conte, M.M.[Mary M.],
Local image statistics: Maximum-entropy constructions and perceptual salience,
JOSA-A(29), No. 7, July 2012, pp. 1313-1345.
WWW Link. 1208
BibRef

Emami, M.[Mohsen], Hoberock, L.L.[Lawrence L.],
Selection of a best metric and evaluation of bottom-up visual saliency models,
IVC(31), No. 10, 2013, pp. 796-808.
Elsevier DOI 1310
Bottom-up saliency mechanism. Which metric for particular model. BibRef

Chuang, Y.L.[Yue-Long], Chen, L.[Ling], Chen, G.C.[Gen-Cai], Woodward, J.[John],
Isophote Based Center-Surround Contrast Computation for Image Saliency Detection,
IEICE(E97-D), No. 1, January 2013, pp. 160-163.
WWW Link. 1402
BibRef

Alsam, A.[Ali], Sharma, P.[Puneet],
Robust metric for the evaluation of visual saliency algorithms,
JOSA-A(31), No. 3, March 2014, pp. 532-540.
DOI Link 1403
Fourier optics and signal processing BibRef

Cheng, M.M.[Ming-Ming], Mitra, N.J.[Niloy J.], Huang, X.L.[Xiao-Lei], Hu, S.M.[Shi-Min],
SalientShape: group saliency in image collections,
VC(30), No. 4, April 2014, pp. 443-453.
WWW Link. 1404
BibRef

Tong, N., Lu, H.C.[Hu-Chuan], Zhang, L.[Lihe], Ruan, X.[Xiang],
Saliency Detection with Multi-Scale Superpixels,
SPLetters(21), No. 9, Sept 2014, pp. 1035-1039.
IEEE DOI 1406
Bayes methods BibRef

Qin, Y.[Yao], Feng, M.Y.[Meng-Yang], Lu, H.C.[Hu-Chuan], Cottrell, G.W.[Garrison W.],
Hierarchical Cellular Automata for Visual Saliency,
IJCV(126), No. 7, July 2018, pp. 751-770.
Springer DOI 1806
BibRef

Tong, N.[Na], Lu, H.C.[Hu-Chuan], Zhang, Y.[Ying], Ruan, X.[Xiang],
Salient object detection via global and local cues,
PR(48), No. 10, 2015, pp. 3258-3267.
Elsevier DOI 1507
Visual saliency BibRef

Wu, Y.X.[Yang-Xi], Zhang, D.B.[Dong-Bo], Yin, F.[Feng], Zhang, Y.[Ying],
Salient object detection based on global to local visual search guidance,
SP:IC(102), 2022, pp. 116618.
Elsevier DOI 2202
Salient object detection, Visual attention prediction, Visual search guidance BibRef

Wang, Z.[Zheng], Zhou, Z.[Ziqi], Lu, H.C.[Hu-Chuan], Jiang, J.M.[Jian-Min],
Global and local sensitivity guided key salient object re-augmentation for video saliency detection,
PR(103), 2020, pp. 107275.
Elsevier DOI 2005
Video saliency detection, Ranking saliency, Semantical guidance BibRef

Zhang, Y.[Ying], Lu, H.C.[Hu-Chuan], Zhang, L.[Lihe], Ruan, X.[Xiang],
Combining motion and appearance cues for anomaly detection,
PR(51), No. 1, 2016, pp. 443-452.
Elsevier DOI 1601
Anomaly detection BibRef

Zhang, Y.[Ying], Lu, H.C.[Hu-Chuan], Zhang, L.[Lihe], Ruan, X.[Xiang], Sakai, S.[Shun],
Video anomaly detection based on locality sensitive hashing filters,
PR(59), No. 1, 2016, pp. 302-311.
Elsevier DOI 1609
Anomaly detection BibRef

Lu, H.C.[Hu-Chuan], Zhang, X., Qi, J., Tong, N., Ruan, X.[Xiang], Yang, M.H.[Ming-Hsuan],
Co-Bootstrapping Saliency,
IP(26), No. 1, January 2017, pp. 414-425.
IEEE DOI 1612
feature extraction BibRef

Lu, H.C.[Hu-Chuan], Li, X.H.[Xiao-Hui], Zhang, L.[Lihe], Ruan, X.[Xiang], Yang, M.H.[Ming-Hsuan],
Dense and Sparse Reconstruction Error Based Saliency Descriptor,
IP(25), No. 4, April 2016, pp. 1592-1603.
IEEE DOI 1604
BibRef
Earlier: A2, A1, A3, A4, A5:
Saliency Detection via Dense and Sparse Reconstruction,
ICCV13(2976-2983)
IEEE DOI 1403
Bayes methods BibRef

Zhang, L.[Lihe], Yang, C., Lu, H.C.[Hu-Chuan], Ruan, X.[Xiang], Yang, M.H.[Ming-Hsuan],
Ranking Saliency,
PAMI(39), No. 9, September 2017, pp. 1892-1904.
IEEE DOI 1708
Computational modeling, Image color analysis, Image segmentation, Labeling, Manifolds, Visualization, Saliency detection, manifold ranking, multi-scale, graph BibRef

Manipoonchelvi, P., Muneeswaran, K.,
Region-based saliency detection,
IET-IPR(8), No. 9, September 2014, pp. 519-527.
DOI Link 1410
image resolution BibRef

Maggiori, E., Lotito, P., Manterola, H.L., del Fresno, M.,
Comments on 'A Closed-Form Solution to Tensor Voting: Theory and Applications',
PAMI(36), No. 12, December 2014, pp. 2567-2568.
IEEE DOI 1411
Closed-form solutions
See also Closed-Form Solution to Tensor Voting: Theory and Applications, A. BibRef

Xia, C.[Chen], Qi, F.[Fei], Shi, G.M.[Guang-Ming], Wang, P.J.[Peng-Jin],
Nonlocal Center-Surround Reconstruction-Based Bottom-Up Saliency Estimation,
PR(48), No. 4, 2015, pp. 1337-1348.
Elsevier DOI 1502
BibRef
Earlier: A1, A4, A2, A3: ICIP13(206-210)
IEEE DOI 1402
Saliency Compressed sensing BibRef

Zhang, X.J.[Xiu-Jun], Xu, C.[Chen], Li, M.[Min], Teng, R.K.F.[Robert K.F.],
Study of visual saliency detection via nonlocal anisotropic diffusion equation,
PR(48), No. 4, 2015, pp. 1315-1327.
Elsevier DOI 1502
Saliency detection BibRef

Filipe, S., Itti, L., Alexandre, L.A.,
BIK-BUS: Biologically Motivated 3D Keypoint Based on Bottom-Up Saliency,
IP(24), No. 1, January 2015, pp. 163-175.
IEEE DOI 1502
computational complexity BibRef

Luo, Y.K.[Yong-Kang], Wang, P.[Peng], Zhu, W.J.[Wen-Jun], Qiao, H.[Hong],
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image colour analysis BibRef

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image processing. Coherence BibRef

Xu, L.F.[Lin-Feng], Zeng, L.Y.[Liao-Yuan], Duan, H.P.[Hui-Ping],
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Saliency detection BibRef

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Salient object detection BibRef

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IEEE DOI 1502
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Region-based saliency map BibRef

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Ray Saliency: Bottom-Up Visual Saliency for a Rotating and Zooming Camera,
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Springer DOI 1602
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Zhou, X., Liu, Z., Sun, G., Ye, L., Wang, X.,
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IEEE DOI 1604
Adaptation models BibRef

Dong, Y., Pourazad, M.T., Nasiopoulos, P.,
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Zhao, T.[Tong], Li, L.[Lin], Ding, X.H.[Xing-Hao], Huang, Y.[Yue], Zeng, D.[Delu],
Saliency Detection With Spaces of Background-Based Distribution,
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IEEE DOI 1604
Bayes methods BibRef

Ge, C.J.[Chen-Jie], Fu, K.[Keren], Liu, F.H.[Fang-Hui], Bai, L.[Li], Yang, J.[Jie],
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SP:IC(44), No. 1, 2016, pp. 69-83.
Elsevier DOI 1605
Co-saliency detection BibRef

Ge, C.J.[Chen-Jie], Fu, K.[Keren], Li, Y.J.[Yi-Jun], Yang, J.[Jie], Shi, P.F.[Peng-Fei], Bai, L.[Li],
Co-saliency detection via similarity-based saliency propagation,
ICIP15(1845-1849)
IEEE DOI 1512
Co-saliency detection BibRef

Chen, D.Y.[Dong-Yue], Jia, T.[Tong], Wu, C.D.[Cheng-Dong],
Visual saliency detection: From space to frequency,
SP:IC(44), No. 1, 2016, pp. 57-68.
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Saliency detection BibRef

Qi, W.[Wei], Han, J.[Jing], Zhang, Y.[Yi], Bai, L.F.[Lian-Fa],
Graph-Boolean Map for salient object detection,
SP:IC(49), No. 1, 2016, pp. 9-16.
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Saliency detection BibRef

Tang, H., Chen, C., Pei, X.,
Visual Saliency Detection via Sparse Residual and Outlier Detection,
SPLetters(23), No. 12, December 2016, pp. 1736-1740.
IEEE DOI 1612
image filtering BibRef

Qi, W.[Wei], Han, J.[Jing], Zhang, Y.[Yi], Bai, L.[Lianfa],
Saliency detection via Boolean and foreground in a dynamic Bayesian framework,
VC(33), No. 2, February 2017, pp. 209-220.
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Huang, R., Feng, W., Sun, J.,
Color Feature Reinforcement for Cosaliency Detection Without Single Saliency Residuals,
SPLetters(24), No. 5, May 2017, pp. 569-573.
IEEE DOI 1704
feature extraction BibRef

Xiao, Y.[Yun], Wang, L.M.[Liang-Min], Jiang, B.[Bo], Tu, Z.Z.[Zheng-Zheng], Tang, J.[Jin],
A global and local consistent ranking model for image saliency computation,
JVCIR(46), No. 1, 2017, pp. 199-207.
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Saliency, detection BibRef

Zhang, L.B.[Li-Bao], Lv, X.R.[Xin-Ran], Liang, X.[Xu],
Saliency Analysis via Hyperparameter Sparse Representation and Energy Distribution Optimization for Remote Sensing Images,
RS(9), No. 6, 2017, pp. xx-yy.
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Li, N.Y.[Nian-Yi], Ye, J.W.[Jin-Wei], Ji, Y.[Yu], Ling, H.B.[Hai-Bin], Yu, J.Y.[Jing-Yi],
Saliency Detection on Light Field,
PAMI(39), No. 8, August 2017, pp. 1605-1616.
IEEE DOI 1707
Cluttered backgrounds, similar foreground/background. Cameras, Databases, Image color analysis, Object detection, Robustness, Spatial resolution, Lytro, Saliency detection, focus stack, light field. BibRef

Li, N.Y.[Nian-Yi], Sun, B.[Bilin], Yu, J.Y.[Jing-Yi],
A weighted sparse coding framework for saliency detection,
CVPR15(5216-5223)
IEEE DOI 1510
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Dou, H., Ming, D., Yang, Z., Pan, Z., Li, Y., Tian, J.,
Object-Based Visual Saliency via Laplacian Regularized Kernel Regression,
MultMed(19), No. 8, August 2017, pp. 1718-1729.
IEEE DOI 1708
Biological system modeling, Computational modeling, Kernel, Laplace equations, Object detection, Visualization, Kernel regression, Laplacian regularized kernel regression (LKR), salient object detection, visual saliency BibRef

Chen, J.Z.[Jia-Zhong], Chen, J.[Jie], Cao, H.[Hua], Li, R.[Rong], Xia, T.[Tao], Ling, H.[Hefei], Chen, Y.[Yang],
Saliency detection using suitable variant of local and global consistency,
IET-CV(11), No. 6, September 2017, pp. 479-487.
DOI Link 1709
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Wang, Y.Y.[Yi-Yang], Liu, R.S.[Ri-Sheng], Song, X.L.[Xiao-Liang], Su, Z.X.[Zhi-Xun],
A nonlocal L0 model with regression predictor for saliency detection and extension,
VC(33), No. 11, November 2017, pp. 1467-1482.
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Aytekin, C.[Caglar], Iosifidis, A.[Alexandros], Gabbouj, M.[Moncef],
Probabilistic saliency estimation,
PR(74), No. 1, 2018, pp. 359-372.
Elsevier DOI 1711
Saliency BibRef

Aytekin, C.[Caglar], Ozan, E.C.[Ezgi Can], Kiranyaz, S.[Serkan], Gabbouj, M.[Moncef],
Visual saliency by extended quantum cuts,
ICIP15(1692-1696)
IEEE DOI 1512
Salience map generation. BibRef

Rabbani, N.[Navid], Nazari, B.[Behzad], Sadri, S.[Saeid], Rikhtehgaran, R.[Reyhaneh],
Efficient Bayesian approach to saliency detection based on Dirichlet process mixture,
IET-IPR(11), No. 11, November 2017, pp. 1103-1113.
DOI Link 1711
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Song, R.[Ran], Liu, Y.H.[Yong-Huai], Martin, R.R.[Ralph R.], Echavarria, K.R.[Karina Rodriguez],
Local-to-global mesh saliency,
VC(34), No. 3, March 2018, pp. 323-336.
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Song, R.[Ran], Liu, Y.H.[Yong-Huai], Zhao, Y.T.[Yi-Tian], Martin, R.R.[Ralph R.], Rosin, P.L.[Paul L.],
Conditional random field-based mesh saliency,
ICIP12(637-640).
IEEE DOI 1302
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Bhattacharya, S., Venkatesh, K.S., Gupta, S.,
Visual Saliency Detection Using Spatiotemporal Decomposition,
IP(27), No. 4, April 2018, pp. 1665-1675.
IEEE DOI 1802
feature extraction, object detection, object tracking, video signal processing, blob, detected salient regions, saliency detection BibRef

Li, J.X.[Jun-Xia], Rajan, D.[Deepu], Yang, J.[Jian],
Locality and context-aware top-down saliency,
IET-IPR(12), No. 3, March 2018, pp. 400-407.
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Ren, J.R.[Jing-Ru], Liu, Z.[Zhi], Zhou, X.F.[Xiao-Fei], Sun, G.L.[Guang-Ling], Bai, C.[Cong],
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JVCIR(50), 2018, pp. 227-236.
Elsevier DOI 1802
Saliency integration, Saliency propagation, Similar image, Saliency model BibRef

Zhang, M.[Ming], Pang, Y.[Yu], Wu, Y.H.[Yun-He], Du, Y.[Yue], Sun, H.[Hui], Zhang, K.[Ke],
Saliency detection via local structure propagation,
JVCIR(52), 2018, pp. 131-142.
Elsevier DOI 1804
Saliency detection, Coarse-to-fine, Local structure propagation, Color distribution map, Multi-prior BibRef

Zhang, M.[Ming], Wu, Y.H.[Yun-He], Du, Y.[Yue], Fang, L.[Lei], Pang, Y.[Yu],
Saliency detection integrating global and local information,
JVCIR(53), 2018, pp. 215-223.
Elsevier DOI 1805
Feature similarity metric, Global and local information, Locality-based coding method, Integration mechanism BibRef

Lin, H.B.[Hong-Bin], Wu, Z.[Zheng], Lei, D.[Dong], Wang, W.[Wei], Peng, X.U.[Xi-Uping],
Research on Analytical Solution Tensor Voting,
IEICE(E101-D), No. 3, March 2018, pp. 817-820.
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Annum, R.[Rabbia], Riaz, M.M.[M. Mohsin], Ghafoor, A.[Abdul],
Saliency detection using contrast enhancement and texture smoothing operations,
SIViP(12), No. 3, March 2018, pp. 505-511.
Springer DOI 1804
Low-contrast, small object. BibRef

Yan, Y.J.[Yi-Jun], Ren, J.C.[Jin-Chang], Sun, G.Y.[Gen-Yun], Zhao, H.M.[Hui-Min], Han, J.W.[Jun-Wei], Li, X.L.[Xue-Long], Marshall, S.[Stephen], Zhan, J.[Jin],
Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement,
PR(79), 2018, pp. 65-78.
Elsevier DOI 1804
Background connectivity, Gestalt laws guided optimization, Image saliency detection, Feature fusion, Human vision perception BibRef

Bylinskii, Z.[Zoya], Judd, T.[Tilke], Oliva, A.[Aude], Torralba, A.B.[Antonio B.], Durand, F.[Frédo],
What Do Different Evaluation Metrics Tell Us About Saliency Models?,
PAMI(41), No. 3, March 2019, pp. 740-757.
IEEE DOI 1902
Measurement, Computational modeling, Analytical models, Visualization, Benchmark testing, Observers, Task analysis, saliency applications BibRef

Bylinskii, Z.[Zoya], Recasens, A.[Adrià], Borji, A.[Ali], Oliva, A.[Aude], Torralba, A.B.[Antonio B.], Durand, F.[Frédo],
Where Should Saliency Models Look Next?,
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Hu, S.L.[Sheng-Li], Borji, A.[Ali],
Understanding Perceptual and Conceptual Fluency at a Large Scale,
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Springer DOI 1810
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Huang, K., Zhu, C., Li, G.,
Saliency Detection by Adaptive Channel Fusion,
SPLetters(25), No. 7, July 2018, pp. 1059-1063.
IEEE DOI 1807
Fourier transforms, feature extraction, frequency-domain analysis, object detection, object recognition, saliency detection BibRef

El-Laham, Y., Elvira, V., Bugallo, M.F.,
Robust Covariance Adaptation in Adaptive Importance Sampling,
SPLetters(25), No. 7, July 2018, pp. 1049-1053.
IEEE DOI 1807
covariance matrices, importance sampling, Monte Carlo methodology, adaptive importance sampling, weight degeneracy BibRef

Fidalgo, E.[Eduardo], Alegre, E.[Enrique], González-Castro, V.[Victor], Fernández-Robles, L.[Laura],
Boosting image classification through semantic attention filtering strategies,
PRL(112), 2018, pp. 176-183.
Elsevier DOI 1809
Saliency map, Bag of words, Mean shift, Support vector machine, Image classification BibRef

Azaza, A.[Aymen], van de Weijer, J.[Joost], Douik, A.[Ali], Masana, M.[Marc],
Context proposals for saliency detection,
CVIU(174), 2018, pp. 1-11.
Elsevier DOI 1812
Computational saliency, Object segmentation, Object proposals BibRef

Jian, M.[Muwei], Zhang, W.Y.[Wen-Yin], Yu, H.[Hui], Cui, C.R.[Chao-Ran], Nie, X.S.[Xiu-Shan], Zhang, H.X.[Hua-Xiang], Yin, Y.L.[Yi-Long],
Saliency detection based on directional patches extraction and principal local color contrast,
JVCIR(57), 2018, pp. 1-11.
Elsevier DOI 1812
Saliency detection, Wavelet frame transform, Principal local color contrast, Directional patches BibRef

Shan, D.J.[Dong-Jing], Zhang, X.W.[Xiong-Wei], Zhang, C.[Chao],
Visual saliency based on extended manifold ranking and third-order optimization refinement,
PRL(116), 2018, pp. 1-7.
Elsevier DOI 1812
saliency detection, manifold ranking, graphical model, image segmentation BibRef

Rajankar, O.S.[Omprakash S.], Kolekar, U.D.[Uttam D.], Talbar, S.N.[Sanjay N.],
Heuristics approach to speeding up saliency detection,
SIViP(13), No. 3, April 2019, pp. 465-473.
Springer DOI 1904
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Yang, C.L.[Chun-Lei], Pu, J.X.[Jie-Xin], Dong, Y.S.[Yong-Sheng], Xie, G.S.[Guo-Sen], Si, Y.[Yanna], Liu, Z.H.[Zhong-Hua],
Scene classification-oriented saliency detection via the modularized prescription,
VC(35), No. 4, April 2019, pp. 473-488.
Springer DOI 1906
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Tan, K.[Kai], Wu, Q.B.[Qing-Bo], Meng, F.M.[Fan-Man], Xu, L.F.[Lin-Feng],
Multi Information Fusion Network for Saliency Quality Assessment,
IEICE(E102-D), No. 5, May 2019, pp. 1111-1114.
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Estimating the objective quality of a saliency map. BibRef

Constantin, M.G.[Mihai Gabriel], Redi, M.[Miriam], Zen, G.[Gloria], Ionescu, B.[Bogdan],
Computational Understanding of Visual Interestingness Beyond Semantics: Literature Survey and Analysis of Covariates,
Surveys(51), No. 1, February 2019, pp. Article No 25.
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visual interestingness BibRef

Jia, N.[Ning], Liu, X.H.[Xian-Hui], Zhao, W.D.[Wei-Dong], Zhang, H.T.[Hao-Tian], Zhuo, K.Q.A.[Ke-Qi-Ang],
An adaptive framework for saliency detection,
IJIST(29), No. 3, September 2019, pp. 382-393.
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Xu, L.J.[Li-Juan], Ji, Z.H.[Zhi-Hang], Dempere-Marco, L.[Laura], Wang, F.[Fan], Hu, X.P.[Xiao-Peng],
Gestalt-grouping based on path analysis for saliency detection,
SP:IC(78), 2019, pp. 9-20.
Elsevier DOI 1909
Gestalt-grouping, Smoothest path-based distance, Topological connectedness, Salient region detection BibRef

Jian, M.[Muwei], Zhou, Q.[Quan], Cui, C.R.[Chao-Ran], Nie, X.S.[Xiu-Shan], Luo, H.J.[Han-Jiang], Zhao, J.L.[Jian-Li], Yin, Y.L.[Yi-Long],
Assessment of feature fusion strategies in visual attention mechanism for saliency detection,
PRL(127), 2019, pp. 37-47.
Elsevier DOI 1911
Saliency detection, Background cue, Compactness feature, Fusion strategy BibRef

Piao, Y., Li, X., Zhang, M., Yu, J., Lu, H.,
Saliency Detection via Depth-Induced Cellular Automata on Light Field,
IP(29), No. 1, 2020, pp. 1879-1889.
IEEE DOI 1912
Saliency detection, Image color analysis, Automata, depth-induced cellular automata (DCA) model BibRef

Zhao, Y.F.[Yu-Fei], Song, Y.[Yong], Li, X.[Xu], Sulaman, M.[Muhammad], Guo, Z.K.[Zheng-Kun], Yang, X.[Xin], Wang, F.N.[Feng-Ning], Hao, Q.[Qun],
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JVCIR(66), 2020, pp. 102706.
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Saliency detection, IR images, Bayes formula, Visual attention BibRef

Deng, C., Yang, X., Nie, F., Tao, D.,
Saliency Detection via a Multiple Self-Weighted Graph-Based Manifold Ranking,
MultMed(22), No. 4, April 2020, pp. 885-896.
IEEE DOI 2004
Saliency detection, Feature extraction, Manifolds, Image color analysis, Task analysis, Image reconstruction, self-adaptive weight BibRef

Zhou, X.F.[Xiao-Fei], Li, G.Y.[Gong-Yang], Gong, C.[Chen], Liu, Z.[Zhi], Zhang, J.Y.[Ji-Yong],
Attention-guided RGBD saliency detection using appearance information,
IVC(95), 2020, pp. 103888.
Elsevier DOI 2004
RGBD, Saliency, Bottom-up, Top-down, Attention, Appearance BibRef

Wang, Y.F.[Yong-Fang], Ye, P.[Peng], Xia, Y.M.[Yu-Meng], An, P.[Ping],
A heuristic framework for perceptual saliency prediction,
JVCIR(73), 2020, pp. 102913.
Elsevier DOI 2012
Saliency prediction, Orientation selectivity, Visual acuity, Visual error sensitivity, Free energy principle BibRef

Xu, M., Yang, L., Tao, X., Duan, Y., Wang, Z.,
Saliency Prediction on Omnidirectional Image With Generative Adversarial Imitation Learning,
IP(30), 2021, pp. 2087-2102.
IEEE DOI 2101
Head, Predictive models, Visualization, Task analysis, Semantics, Feature extraction, Omnidirectional images, large-scale dataset, imitation learning BibRef

Ren, D.[Dakai], Wen, X.M.[Xiang-Ming], Jia, T.[Tao], Chen, J.Z.[Jia-Zhong], Li, Z.Y.[Zong-Yi],
Saliency detection via cross-scale deep inference,
JVCIR(75), 2021, pp. 103031.
Elsevier DOI 2103
Cross-scale deep inference, Multi-layer attention, Image saliency, Deep learning BibRef

Li, W.P.[Wei-Peng], Yang, X.G.[Xiao-Gang], Li, C.X.[Chuan-Xiang], Lu, R.T.[Rui-Tao], Xie, X.L.[Xue-Li],
Fast visual saliency based on multi-scale difference of Gaussians fusion in frequency domain,
IET-IPR(14), No. 16, 19 December 2020, pp. 4039-4048.
DOI Link 2103
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Wang, Z.Q.[Zi-Qiang], Liu, Z.[Zhi], Wei, W.J.[Wei-Jie], Duan, H.Z.[Hui-Zhan],
SalED: Saliency prediction with a pithy encoder-decoder architecture sensing local and global information,
IVC(109), 2021, pp. 104149.
Elsevier DOI 2105
Saliency prediction, Fixation prediction, Convolutional neural networks, Encoder-decoder BibRef

Zhang, J.[Jing], Dai, Y.C.[Yu-Chao], Zhang, T.[Tong], Harandi, M.[Mehrtash], Barnes, N.M.[Nick M.], Hartley, R.I.[Richard I.],
Learning Saliency From Single Noisy Labelling: A Robust Model Fitting Perspective,
PAMI(43), No. 8, August 2021, pp. 2866-2873.
IEEE DOI 2107
Noise measurement, Labeling, Predictive models, Annotations, Training, Task analysis, Saliency detection, Salinecy prediction, robust model fitting BibRef

Chao, F.Y.[Fang-Yi], Zhang, L.[Lu], Hamidouche, W.[Wassim], Déforges, O.[Olivier],
A Multi-FoV Viewport-Based Visual Saliency Model Using Adaptive Weighting Losses for 360° Images,
MultMed(23), 2021, pp. 1811-1826.
IEEE DOI 2107
Feature extraction, Adaptation models, Visualization, Measurement, Predictive models, Videos, deep learning BibRef

Wang, F.[Fan], Peng, G.H.[Guo-Hua],
Saliency detection via coarse-to-fine diffusion-based compactness with weighted learning affinity matrix,
JVCIR(78), 2021, pp. 103151.
Elsevier DOI 2107
Saliency detection, Diffusion-based compactness, Multi-view graphs, Weighted learning affinity matrix BibRef

Peng, P.[Peng], Yang, K.F.[Kai-Fu], Luo, F.Y.[Fu-Ya], Li, Y.J.[Yong-Jie],
Saliency Detection Inspired by Topological Perception Theory,
IJCV(129), No. 8, August 2021, pp. 2352-2374.
Springer DOI 2108
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Jiang, C.X.[Chun-Xu], Liu, Y.[Yu], Sun, J.L.[Jing-Lin], Guo, J.C.[Ji-Chang], Lu, W.[Wei],
Illumination-based adaptive saliency detection network through fusion of multi-source features,
JVCIR(79), 2021, pp. 103192.
Elsevier DOI 2109
Multi-source, Illumination discrimination, Salient object detection, Deep learning BibRef

Sasibhooshan, R.[Reshmi], Kumaraswamy, S.[Suresh], Sasidharan, S.[Santhoshkumar],
WavNet: Visual saliency detection using Discrete Wavelet Convolutional Neural Network,
JVCIR(79), 2021, pp. 103236.
Elsevier DOI 2109
Visual saliency detection, Discrete wavelet convolutional neural network, Edge structural similarity loss BibRef

Figueroa-Flores, C.[Carola], Berga, D.[David], van de Weijer, J.[Joost], Raducanu, B.[Bogdan],
Saliency for free: Saliency prediction as a side-effect of object recognition,
PRL(150), 2021, pp. 1-7.
Elsevier DOI 2109
Saliency maps, Unsupervised learning, Object recognition BibRef

Xia, C.X.[Chen-Xing], Gao, X.J.[Xiu-Ju], Fang, X.J.[Xian-Jin], Li, K.C.[Kuan-Ching], Su, S.Z.[Shu-Zhi], Zhang, H.T.[Hai-Tao],
RLP-AGMC: Robust label propagation for saliency detection based on an adaptive graph with multiview connections,
SP:IC(98), 2021, pp. 116372.
Elsevier DOI 2109
Deep features, Multiview connections, Graph affinity matrix, Label propagation, Salient object detection BibRef

Zhou, F.[Fei], Chen, J.H.[Jun-Hua], Liu, B.Z.[Bo-Zhi],
Visual Saliency via Selecting and Reweighting Features in Hierarchical Fusion Network,
SPLetters(28), 2021, pp. 1749-1753.
IEEE DOI 2109
Feature extraction, Visualization, Predictive models, Computational modeling, Task analysis, saliency prediction BibRef

Zeng, H.T.[Hai-Tao], Song, X.H.[Xin-Hang], Chen, G.W.[Gong-Wei], Jiang, S.Q.[Shu-Qiang],
Amorphous Region Context Modeling for Scene Recognition,
MultMed(24), 2022, pp. 141-151.
IEEE DOI 2202
Semantics, Feature extraction, Image segmentation, Convolution, Context modeling, Saliency detection, Layout, Graph neural network, semantic segmentation BibRef

Zabihi, S.[Samad], Tavakoli, H.R.[Hamed R.], Borji, A.[Ali], Mansoori, E.[Eghbal],
A compact deep architecture for real-time saliency prediction,
SP:IC(104), 2022, pp. 116671.
Elsevier DOI 2204
Fast saliency prediction, Deep convolutional neural network, Transfer learning, Compact architecture, Real-time application BibRef

Lai, Q.X.[Qiu-Xia], Zhou, T.F.[Tian-Fei], Khan, S.[Salman], Sun, H.Q.[Han-Qiu], Shen, J.B.[Jian-Bing], Shao, L.[Ling],
Weakly Supervised Visual Saliency Prediction,
IP(31), 2022, pp. 3111-3124.
IEEE DOI 2205
Visualization, Semantics, Computational modeling, Biological system modeling, Annotations, Data models, deep learning BibRef

Wang, F.[Fan], Peng, G.H.[Guo-Hua],
Graph construction by incorporating local and global affinity graphs for saliency detection,
SP:IC(105), 2022, pp. 116712.
Elsevier DOI 2205
Saliency detection, Graph construction, Multi-view features, Joint global affinity matrix, Local affinity graph BibRef

Liu, Y.[Yi], Zhang, D.W.[Ding-Wen], Zhang, Q.[Qiang], Han, J.G.[Jun-Gong],
Part-Object Relational Visual Saliency,
PAMI(44), No. 7, July 2022, pp. 3688-3704.
IEEE DOI 2206
Object detection, Routing, Feature extraction, Streaming media, Training, Task analysis, Saliency detection, part-object relationships BibRef

Yan, K.[Ke], Wang, X.Y.[Xiu-Ying], Kim, J.M.[Jin-Man], Zuo, W.M.[Wang-Meng], Feng, D.D.[David Dagan],
Deep Cognitive Gate: Resembling Human Cognition for Saliency Detection,
PAMI(44), No. 9, September 2022, pp. 4776-4792.
IEEE DOI 2208
Cognition, Saliency detection, Visualization, Feature extraction, Logic gates, Benchmark testing, Heating systems, Cognition, object detection BibRef

Zhang, K.[Kao], Chen, Z.Z.[Zhen-Zhong], Li, S.[Songnan], Liu, S.[Shan],
An efficient saliency prediction model for Unmanned Aerial Vehicle video,
PandRS(194), 2022, pp. 152-166.
Elsevier DOI 2212

WWW Link. Visual saliency, UAV video analysis, Spatial-temporal features, Prior information BibRef

Jerripothula, K.R.[Koteswar Rao], Mukherjee, P.[Prerana], Cai, J.F.[Jian-Fei], Lu, S.J.[Shi-Jian], Yuan, J.S.[Jun-Song],
AppFuse: An Appearance Fusion Framework for Saliency Cues,
CirSysVideo(32), No. 12, December 2022, pp. 8261-8274.
IEEE DOI 2212
Gaussian processes, Fuses, Location awareness, Reliability, Computational modeling, Image segmentation, Computer science, co-localization BibRef

Huang, M.[Mengke], Li, G.Y.[Gong-Yang], Liu, Z.[Zhi], Wu, Y.[Yong], Gong, C.[Chen], Zhu, L.C.[Lin-Chao], Yang, Y.[Yi],
Exploring viewport features for semi-supervised saliency prediction in omnidirectional images,
IVC(129), 2023, pp. 104590.
Elsevier DOI 2301
Omnidirectional image, Saliency prediction, Semi-supervised learning BibRef

Lin, H.B.[Hong-Bin], Guo, D.[Dan], Wei, J.N.[Jia-Ning], Guan, B.[Boran], Chen, Z.[Zeyu], Peng, X.P.[Xiu-Ping],
Analytical Tensor Voting in ND Space and its Properties,
PAMI(45), No. 5, May 2023, pp. 5404-5416.
IEEE DOI 2304
Tensors, Noise measurement, Estimation, Aerospace electronics, TV, Robustness, K-sphere, surface integral BibRef

Wang, Z.Q.[Zi-Qiang], Liu, Z.[Zhi], Li, G.Y.[Gong-Yang], Wang, Y.[Yang], Zhang, T.H.[Tian-Hong], Xu, L.H.[Li-Hua], Wang, J.J.[Ji-Jun],
Spatio-Temporal Self-Attention Network for Video Saliency Prediction,
MultMed(25), 2023, pp. 1161-1174.
IEEE DOI 2305
Computational modeling, Visualization, Solid modeling, Task analysis, Semantics, video saliency prediction BibRef

Song, R.[Ran], Zhang, W.[Wei], Zhao, Y.T.[Yi-Tian], Liu, Y.H.[Yong-Huai], Rosin, P.L.[Paul L.],
3D Visual Saliency: An Independent Perceptual Measure or a Derivative of 2D Image Saliency?,
PAMI(45), No. 11, November 2023, pp. 13083-13099.
IEEE DOI 2310
BibRef
Earlier:
Mesh Saliency: An Independent Perceptual Measure or A Derivative of Image Saliency?,
CVPR21(8849-8858)
IEEE DOI 2111
Graphics, Deep learning, Correlation coefficient, Codes, Current measurement BibRef

Kang, M.S.[Min-Soo], Kang, M.[Minkoo], Lee, S.W.[Seong-Whan], Kim, S.[Suhyun],
Mixup Mask Adaptation: Bridging the gap between input saliency and representations via attention mechanism in feature mixup,
IVC(146), 2024, pp. 105013.
Elsevier DOI 2405
Regularization, Data augmentation, Mixup BibRef

Peng, B.[Bo], Lin, T.X.[Tian-Xiang], Jin, D.C.[Deng-Chao], Pan, Z.Q.[Zhao-Qing], Lei, J.J.[Jian-Jun],
Saliency Map-Guided End-to-End Image Coding for Machines,
SPLetters(31), 2024, pp. 1755-1759.
IEEE DOI 2408
Image coding, Task analysis, Object detection, Visualization, Training, Semantics, Machine vision, Image coding for machines, mean square error loss BibRef

Liu, N.[Nian], Luo, Z.Y.[Zi-Yang], Zhang, N.[Ni], Han, J.W.[Jun-Wei],
VST++: Efficient and Stronger Visual Saliency Transformer,
PAMI(46), No. 11, November 2024, pp. 7300-7316.
IEEE DOI 2410
Transformers, Task analysis, Computational modeling, Decoding, Computer architecture, Multitasking, Feature extraction, saliency detection BibRef

Yang, Q.[Qin], Gao, W.X.[Wen-Xuan], Li, C.L.[Cheng-Lin], Wang, H.[Hao], Dai, W.R.[Wen-Rui], Zou, J.[Junni], Xiong, H.K.[Hong-Kai], Frossard, P.[Pascal],
360Spred: Saliency Prediction for 360-Degree Videos Based on 3D Separable Graph Convolutional Networks,
CirSysVideo(34), No. 10, October 2024, pp. 9979-9996.
IEEE DOI 2411
Feature extraction, Optical flow, Convolution, Streaming media, Data mining, Predictive models, Graph convolution, 3D convolution, 360-degree videos BibRef

Qiu, L.R.[Lin-Run], Zhang, D.B.[Dong-Bo], Hu, Y.K.[Ying-Kun],
Research on image saliency detection based on deep neural network,
IET-IPR(18), No. 12, 2024, pp. 3393-3402.
DOI Link 2411
edge detection, feature extraction, image matching, neural nets BibRef

Yasarla, R.[Rajeev], Weng, R.L.[Ren-Liang], Choi, W.[Wongun], Patel, V.M.[Vishal M.], Sadeghian, A.[Amir],
3SD: Self-Supervised Saliency Detection With No Labels,
WACV24(312-321)
IEEE DOI 2404
Training, Annotations, Image edge detection, Semantics, Self-supervised learning, Object detection, Benchmark testing, Image recognition and understanding BibRef

Djilali, Y.A.D.[Yasser Abdelaziz Dahou], McGuinness, K.[Kevin], O'Connor, N.[Noel],
Learning Saliency From Fixations,
WACV24(382-392)
IEEE DOI 2404
Measurement, Pipelines, Focusing, Computer architecture, Benchmark testing, Predictive models, Algorithms BibRef

Daroya, R.[Rangel], Sun, A.[Aaron], Maji, S.[Subhransu],
COSE: A Consistency-Sensitivity Metric for Saliency on Image Classification,
VIPriors23(149-158)
IEEE DOI 2401
BibRef

Wang, S.W.[Shou-Wen], Wan, Q.[Qian], Xiang, X.[Xiang], Zeng, Z.G.[Zhi-Gang],
Saliency Regularization for Self-Training with Partial Annotations,
ICCV23(1611-1620)
IEEE DOI 2401
BibRef

Lin, X.[Xu], Qing, C.M.[Chun-Mei], Tan, J.P.[Jun-Peng], Xu, X.M.[Xiang-Min],
Multi-Scale Transformer Network for Saliency Prediction on 360-Degree Images,
ICIP23(1700-1704)
IEEE DOI 2312
BibRef

Woerl, A.C.[Ann-Christin], Disselhoff, J.[Jan], Wand, M.[Michael],
Initialization Noise in Image Gradients and Saliency Maps,
CVPR23(1766-1775)
IEEE DOI 2309
BibRef

Morrison, K.[Katelyn], Mehra, A.[Ankita], Perer, A.[Adam],
Shared Interest ... Sometimes: Understanding the Alignment between Human Perception, Vision Architectures, and Saliency Map Techniques,
XAI4CV23(3776-3781)
IEEE DOI 2309
BibRef

Kikuchi, A.[Atsushi], Uchida, K.[Kotaro], Waga, M.[Masaki], Suenaga, K.[Kohei],
Borex: Bayesian-optimization-based Refinement of Saliency Map for Image- and Video-classification Models,
ACCV22(VII:274-290).
Springer DOI 2307
BibRef

Englebert, A.[Alexandre], Cornu, O.[Olivier], de Vleeschouwer, C.[Christophe],
Backward Recursive Class Activation Map Refinement for High Resolution Saliency Map,
ICPR22(2444-2450)
IEEE DOI 2212
Measurement, Location awareness, Deep learning, Backpropagation, Visualization, Image resolution, Art BibRef

Tursun, O.[Osman], Denman, S.[Simon], Sridharan, S.[Sridha], Fookes, C.[Clinton],
SESS: Saliency Enhancing with Scaling and Sliding,
ECCV22(XII:318-333).
Springer DOI 2211
BibRef

Hussain, T.[Tanveer], Anwar, A.[Abbas], Anwar, S.[Saeed], Petersson, L.[Lars], Baik, S.W.[Sung Wook],
Pyramidal Attention for Saliency Detection,
FaDE-TCV22(2877-2887)
IEEE DOI 2210
Convolutional codes, Training, Fuses, Predictive models, Feature extraction, Transformers, Data models BibRef

Zhang, N.[Ni], Han, J.W.[Jun-Wei], Liu, N.[Nian], Shao, L.[Ling],
Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection,
ICCV21(4147-4156)
IEEE DOI 2203
Codes, Convolution, Fuses, Scalability, Benchmark testing, Search problems, Low-level and physics-based vision, Scene analysis and understanding BibRef

Luo, S.Y.[Shun-Yan], Barut, E.[Emre], Jin, F.[Fang],
Statistically Consistent Saliency Estimation,
ICCV21(725-733)
IEEE DOI 2203
Legged locomotion, Deep learning, Analytical models, Upper bound, Computational modeling, Perturbation methods, Explainable AI, Visual reasoning and logical representation BibRef

Linardos, A.[Akis], Kümmerer, M.[Matthias], Press, O.[Ori], Bethge, M.[Matthias],
DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling,
ICCV21(12899-12908)
IEEE DOI 2203
Measurement, Protocols, Computational modeling, Transfer learning, Predictive models, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Jalwana, M.A.A.K.[Mohammad A. A. K.], Akhtar, N.[Naveed], Bennamoun, M.[Mohammed], Mian, A.[Ajmal],
CAMERAS: Enhanced Resolution And Sanity preserving Class Activation Mapping for image saliency,
CVPR21(16322-16331)
IEEE DOI 2111
Backpropagation, Measurement, Visualization, Image resolution, Computational modeling, Focusing, Predictive models BibRef

Petsiuk, V.[Vitali], Jain, R.[Rajiv], Manjunatha, V.[Varun], Morariu, V.I.[Vlad I.], Mehra, A.[Ashutosh], Ordonez, V.[Vicente], Saenko, K.[Kate],
Black-box Explanation of Object Detectors via Saliency Maps,
CVPR21(11438-11447)
IEEE DOI 2111
Software testing, Measurement, Location awareness, Visualization, Pathology, Error analysis, Computational modeling BibRef

Ding, G.Q.[Guan-Qun], Imamoglu, N.[Nevrez], Caglayan, A.[Ali], Murakawa, M.[Masahiro], Nakamura, R.[Ryosuke],
FBNet: FeedBack-Recursive CNN for Saliency Detection,
MVA21(1-5)
DOI Link 2109
Deep learning, Visualization, Benchmark testing, Feature extraction, Convolutional neural networks, Feeds BibRef

Zhang, Y.F.[Yi-Feng], Jiang, M.[Ming], Zhao, Q.[Qi],
Saliency Prediction with External Knowledge,
WACV21(484-493)
IEEE DOI 2106
Knowledge engineering, Bridges, Computational modeling, Semantics, Neural networks BibRef

Hu, F.Y.[Fei-Yan], McGuinness, K.[Kevin],
FastSal: a Computationally Efficient Network for Visual Saliency Prediction,
ICPR21(9054-9061)
IEEE DOI 2105
Measurement, Visualization, Computational modeling, Predictive models, Prediction algorithms, BibRef

Mbarki, A., Naouai, M.,
A Marked Point Process Model For Visual Perceptual Groups Extraction,
VCIP20(511-514)
IEEE DOI 2102
Bayes methods, Organizations, Mathematical model, Kernel, Feature extraction, Visual perception, Simulated annealing, marked point process BibRef

Zhou, H., Xie, X., Lai, J., Chen, Z., Yang, L.,
Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection,
CVPR20(9138-9147)
IEEE DOI 2008
Correlation, Saliency detection, Task analysis, Decoding, Linear programming, Silicon, Benchmark testing BibRef

Kapishnikov, A.[Andrei], Bolukbasi, T.[Tolga], Viegas, F.[Fernanda], Terry, M.[Michael],
XRAI: Better Attributions Through Regions,
ICCV19(4947-4956)
IEEE DOI 2004
image representation, image segmentation, neural nets, object detection, attribution methods, XRAI, saliency methods, Birds BibRef

Zeng, Y.[Yu], Zhuge, Y.Z.[Yun-Zhi], Lu, H.C.[Hu-Chuan], Zhang, L.[Lihe], Qian, M.Y.[Ming-Yang], Yu, Y.Z.[Yi-Zhou],
Multi-Source Weak Supervision for Saliency Detection,
CVPR19(6067-6076).
IEEE DOI 2002
BibRef

Mazumdar, P., Battisti, F.,
A Content-Based Approach for Saliency Estimation in 360 Images,
ICIP19(3197-3201)
IEEE DOI 1910
Omni-directional images, saliency, content, global and local features BibRef

Xu, X.[Xin], Wang, J.[Jie],
Extended Non-local Feature for Visual Saliency Detection in Low Contrast Images,
CEFR-LCV18(IV:580-592).
Springer DOI 1905
BibRef

Cheng, H., Chao, C., Dong, J., Wen, H., Liu, T., Sun, M.,
Cube Padding for Weakly-Supervised Saliency Prediction in 360° Videos,
CVPR18(1420-1429)
IEEE DOI 1812
Videos, Distortion, Heating systems, Visualization, Predictive models, Computational modeling BibRef

Wang, C., Fan, Y.,
Saliency Detection using Iterative Dynamic Guided Filtering,
ICPR18(3396-3401)
IEEE DOI 1812
Low pass filters, Maximum likelihood detection, Nonlinear filters, Saliency detection, Image edge detection, contrast model BibRef

Zhang, Z.H.[Zi-Heng], Xu, Y.Y.[Yan-Yu], Yu, J.Y.[Jing-Yi], Gao, S.H.[Sheng-Hua],
Saliency Detection in 360° Videos,
ECCV18(VII: 504-520).
Springer DOI 1810
BibRef

Benois-Pineau, J., Mitrea, M.,
Extraction of saliency in images and video: Problems, methods and applications. A survey,
IPTA17(1-6)
IEEE DOI 1804
cognition, feature extraction, video signal processing, watermarking, visual saliency BibRef

Biswas, S., Fezza, S.A., Larabi, M.C.,
Towards light-compensated saliency prediction for omnidirectional images,
IPTA17(1-6)
IEEE DOI 1804
distortion, image representation, 2D saliency, 360-degree images, conversion problem, distortion compensation, omnidirectional images BibRef

Hwang, I., Jeong, D.J., Park, J.S., Cho, N.I.,
Co-saliency detection via seed propagation over the integrated graph with a cluster layer,
ICIP17(2040-2044)
IEEE DOI 1803
Feature extraction, Histograms, IP networks, Image color analysis, Iris, Nonhomogeneous media, Saliency detection, Co-saliency, seed propagation model BibRef

Xu, N., Guo, Y., Kong, X.,
Saliency detection via local single Gaussian model,
ICIP17(2289-2293)
IEEE DOI 1803
Computational modeling, Covariance matrices, Dictionaries, Image color analysis, Indexes, Reliability, Saliency detection, saliency map BibRef

Le Philippe, N., Itier, V., Puech, W.,
Visual saliency-based confidentiality metric for selective crypto-compressed JPEG images,
ICIP17(4347-4351)
IEEE DOI 1803
Distortion, Encryption, Image quality, Measurement, Transform coding, Visualization, JPEG, confidentiality metric, encryption, visual saliency BibRef

Michaelsen, E., Arens, M.,
Hierarchical Grouping Using Gestalt Assessments,
Symmetry17(1702-1709)
IEEE DOI 1802
Aggregates, Feature extraction, Image color analysis, Image recognition, Reflection BibRef

Michaelsen, E., Arens, M.,
Hierarchical Grouping: The Gestalt Assessments Method,
Symmetry17(1710-1714)
IEEE DOI 1802
Aggregates, Grammar, Heating systems, Image color analysis, Reflection BibRef

Zhu, C.B.[Chun-Biao], Li, G.[Ge], Guo, X.Q.[Xiao-Qiang], Wang, W.M.[Wen-Min], Wang, R.G.[Rong-Gang],
A Multilayer Backpropagation Saliency Detection Algorithm Based on Depth Mining,
CAIP17(II: 14-23).
Springer DOI 1708
BibRef

Li, M.[Meng], Liu, X.[Xing], Tang, L.M.[Li-Ming],
A Phase Field Variational Model with Arctangent Regularization for Saliency Detection,
SoftBio17(29-35)
IEEE DOI 1609
feature extraction, variational techniques, visual perception, arctangent regularization, classical Cahn-Hilliard model, complex image domain, dynamical competition, energy functional minimization, highly anisotropic interfacial energy, human visual perception, phase field variational model, saliency detection, visual attention feature extraction, Computational modeling, Feature extraction, Mathematical model, Visual systems, Visualization BibRef

Jetley, S.[Saumya], Murray, N.[Naila], Vig, E.[Eleonora],
End-to-End Saliency Mapping via Probability Distribution Prediction,
CVPR16(5753-5761)
IEEE DOI 1612
BibRef

Liu, H., Tao, S., Li, Z.,
Saliency detection via global-object-seed-guided cellular automata,
ICIP16(2772-2776)
IEEE DOI 1610
Automata BibRef

Zhang, L., Sun, Q., Chen, J.,
Multi-image saliency analysis via histogram and spectral feature clustering for satellite images,
ICIP16(2802-2806)
IEEE DOI 1610
Histograms;Image processing;clustering;saliency BibRef

Martinez-Rodriguez, D.E.[Diana E.], Ayala-Ramirez, V.[Victor], Hernandez-Belmonte, U.H.[Uriel H.],
Saliency Detection Based on Heuristic Rules,
MCPR16(94-103).
Springer DOI 1608
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Xu, F.[Fei], Xian, M.[Min], Cheng, H.D., Ding, J.R.[Jian-Rui], Zhang, Y.T.[Ying-Tao],
Unsupervised saliency estimation based on robust hypotheses,
WACV16(1-6)
IEEE DOI 1606
Adaptation models BibRef

Tasse, F.P., Kosinka, J., Dodgson, N.,
Cluster-Based Point Set Saliency,
ICCV15(163-171)
IEEE DOI 1602
Computational modeling BibRef

Li, J., Xia, C., Song, Y., Fang, S., Chen, X.,
A Data-Driven Metric for Comprehensive Evaluation of Saliency Models,
ICCV15(190-198)
IEEE DOI 1602
Benchmark testing BibRef

Zeng, Y., Xu, Y.,
Saliency Detection Using Quaternion Sparse Reconstruction,
ACVR15(469-476)
IEEE DOI 1602
Color BibRef

Schauerte, B.[Boris], Wortwein, T.[Torsten], Stiefelhagen, R.[Rainer],
Color decorrelation helps visual saliency detection,
ICIP15(1965-1969)
IEEE DOI 1512
BibRef

Greenberg, S., Chung, A.G., Chwyl, B., Wong, A.,
TIGGER: A Texture-Illumination Guided Global Energy Response Model for Illumination Robust Object Saliency,
CRV16(296-302)
IEEE DOI 1612
Bayesian estimation BibRef

Chwyl, B., Chung, A.G., Li, F.Y., Wong, A., Clausi, D.A.,
TIGER: A texture-illumination guided energy response model for illumination robust local saliency,
ICIP15(1970-1974)
IEEE DOI 1512
Bayesian estimation BibRef

Zhang, H.[Hui], Zhang, J.F.[Jin-Fang], Xu, F.J.[Fan-Jiang],
Land use and land cover classification base on image saliency map cooperated coding,
ICIP15(2616-2620)
IEEE DOI 1512
Bag-of-Words Model BibRef

Liu, Y.Q.[Ya-Qi], Cai, Q.A.[Qi-Ang], Zhu, X.B.[Xiao-Bin], Cao, J.[Jian], Li, H.S.[Hai-Sheng],
Saliency detection using two-stage scoring,
ICIP15(4062-4066)
IEEE DOI 1512
Saliency detection, manifold ranking, random walk, two-stage scoring BibRef

Park, H.S.[Hyun Soo], Shi, J.B.[Jian-Bo],
Social saliency prediction,
CVPR15(4777-4785)
IEEE DOI 1510
BibRef

Luo, Y.[Yan], Wong, Y.K.[Yong-Kang], Zhao, Q.[Qi],
Label Consistent Quadratic Surrogate model for visual saliency prediction,
CVPR15(5060-5069)
IEEE DOI 1510
BibRef

Qin, Y.[Yao], Lu, H.C.[Hu-Chuan], Xu, Y.Q.[Yi-Qun], Wang, H.[He],
Saliency detection via Cellular Automata,
CVPR15(110-119)
IEEE DOI 1510
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Le Meur, O.[Olivier], Liu, Z.[Zhi],
Saliency Aggregation: Does Unity Make Strength?,
ACCV14(IV: 18-32).
Springer DOI 1504
Does aggregation do better then good saliency maps. BibRef

Zhao, B.[Bin], Delp, E.J.[Edward J.],
Visual Saliency Models Based on Spectrum Processing,
WACV15(976-981)
IEEE DOI 1503
Computational modeling. Frequency domain analysis. BibRef

Qi, S.X.[Sheng-Xiang], Yu, J.G.[Jin-Gang], Zhao, J.[Ji], Ma, J.[Jie], Tian, J.W.[Jin-Wen],
Visual saliency detection using feature activity weighted decorrelation cues,
ICIP14(1140-1144)
IEEE DOI 1502
Decorrelation BibRef

Khatoonabadi, S.H.[Sayed Hossein], Bajic, I.V.[Ivan V.], Shan, Y.F.[Yu-Feng],
Comparison of visual saliency models for compressed video,
ICIP14(1081-1085)
IEEE DOI 1502
Computational modeling BibRef

Altamirano-Gómez, G.E.[Gerardo E.], Bayro-Corrochano, E.[Eduardo],
Conformal Geometric Algebra method for detection of geometric primitives,
ICPR16(4190-4195)
IEEE DOI 1705
BibRef
Earlier:
Conformal Geometric Method for Voting,
CIARP14(802-809).
Springer DOI 1411
Extension of Hough or tensor voting. Algebra, Clustering algorithms, Data mining, Feature extraction, Organizations, Silicon, Tensile stress. BibRef

Fathalla, R.[Radwa], Vogiatzis, G.[George],
Detection of multiple meaningful primitive geometric models,
BMVC14(xx-yy).
HTML Version. 1410
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Yan, X.Y.[Xiao-Yun], Wang, Y.H.[Yue-Huan], Song, M.M.[Meng-Meng], Jiang, M.[Man],
Saliency Detection Using Color Spatial Variance Weighted Graph Model,
ACPR13(410-414)
IEEE DOI 1408
computer vision BibRef

Gupta, S., Agrawal, R., Layek, R., Mukhopadhyay, J.,
Psychovisual saliency in color images,
NCVPRIPG13(1-4)
IEEE DOI 1408
computer vision BibRef

Wu, J.[Jie], Zhang, L.Q.[Li-Qing],
Gestalt saliency: Salient region detection based on Gestalt principles,
ICIP13(181-185)
IEEE DOI 1402
Computer vision BibRef

Imamoglu, N.[Nevrez], Fang, Y.M.[Yu-Ming], Yu, W.W.[Wen-Wei], Lin, W.S.[Wei-Si],
2D mel-cepstrum based saliency detection,
ICIP13(236-239)
IEEE DOI 1402
Biological system modeling BibRef

Pan, J.S.[Jin-Shan], Su, Z.X.[Zhi-Xun], Bian, M.R.[Mao-Ran], Liu, R.S.[Ri-Sheng],
Saliency detection based on an edge-preserving filter,
ICIP13(1757-1761)
IEEE DOI 1402
Bayesian framework;Saliency map;edge-preserving filter;image matting BibRef

Zhou, Q.[Quan], Chen, J.[Ji], Ren, S.W.[Shi-Wei], Zhou, Y.[Yu], Chen, J.[Jun], Liu, W.Y.[Wen-Yu],
On contrast combinations for visual saliency detection,
ICIP13(2665-2669)
IEEE DOI 1402
Saliency detection BibRef

Zhang, L.[Lin], Gu, Z.Y.[Zhong-Yi], Li, H.Y.[Hong-Yu],
SDSP: A novel saliency detection method by combining simple priors,
ICIP13(171-175)
IEEE DOI 1402
Accuracy BibRef

Shtrom, E.[Elizabeth], Leifman, G.[George], Tal, A.[Ayellet],
Saliency Detection in Large Point Sets,
ICCV13(3591-3598)
IEEE DOI 1403
Point sets, Saliency, Visual saliency BibRef

Margolin, R.[Ran], Tal, A.[Ayellet], Zelnik-Manor, L.[Lihi],
What Makes a Patch Distinct?,
CVPR13(1139-1146)
IEEE DOI 1309
distinctness, saliency, salient object BibRef

Mai, L.[Long], Niu, Y.Z.[Yu-Zhen], Liu, F.[Feng],
Saliency Aggregation: A Data-Driven Approach,
CVPR13(1131-1138)
IEEE DOI 1309
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Zhou, Z.[Zhen], Huang, Y.Z.[Yong-Zhen], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Depth-embedded multiple pooling for image classification,
ICIP13(4335-4339)
IEEE DOI 1402
Depth Estimation, Image Classification, Multiple Pooling BibRef

Wu, Z.F.[Zi-Feng], Huang, Y.Z.[Yong-Zhen], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Group encoding of local features in image classification,
ICPR12(1505-1508).
WWW Link. 1302
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Zhou, Q.[Quan], Li, N.[Nianyi], Yang, Y.[Yi], Chen, P.[Pan], Liu, W.Y.[Wen-Yu],
Corner-surround Contrast for saliency detection,
ICPR12(1423-1426).
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Zhou, Y.[Yue], Shi, K.[Kun],
Spatiotemporal saliency based on distributed opponent oriented energy,
ICPR12(2021-2024).
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Object-level saliency detection based on spatial compactness assumption,
ICIP13(2475-2479)
IEEE DOI 1402
saliency detection BibRef

Zhang, H.[Hui], Wang, W.Q.[Wei-Qiang], Su, G.P.[Gui-Ping], Duan, L.J.[Li-Juan],
A simple and effective saliency detection approach,
ICPR12(186-189).
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Yeh, H.H.[Hsin-Ho], Chen, C.S.[Chu-Song],
From rareness to compactness: Contrast-aware image saliency detection,
ICIP12(1077-1080).
IEEE DOI 1302
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Yang, W.B.[Wei-Bin], Fang, B.[Bin], Tang, Y.Y.[Yuan Yan], Shang, Z.W.[Zhao-Wei], Zhao, H.J.[Heng-Jun],
Visual saliency estimation using support value transform,
ICIP12(1069-1072).
IEEE DOI 1302
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Narayanan, M.[Maruthi], Kimia, B.B.[Benjamin B.],
Bottom-Up Perceptual Organization of Images into Object Part Hypotheses,
ECCV12(I: 257-271).
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Sharma, G.[Gaurav], Jurie, F.[Frederic], Schmid, C.[Cordelia],
Discriminative spatial saliency for image classification,
CVPR12(3506-3513).
IEEE DOI 1208
Where is the object. BibRef

Gong, D.[Dian], Medioni, G.[Gerard],
Probabilistic tensor voting for robust perceptual grouping,
POCV12(1-8).
IEEE DOI 1207
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Potapova, E.[Ekaterina], Zillich, M.[Michael], Vincze, M.[Markus],
Attention-driven segmentation of cluttered 3D scenes,
ICPR12(3610-3613).
WWW Link. 1302
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Earlier:
Learning What Matters: Combining Probabilistic Models of 2D and 3D Saliency Cues,
CVS11(132-142).
Springer DOI 1109
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ICCV09(2185-2192).
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IEEE DOI 0911
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Cognitive Vision and Perceptual Grouping by Production Systems with Blackboard Control: An Example for High-Resolution SAR-Images,
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Curvature Estimation and Curve Inference with Tensor Voting: A New Approach,
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Tensor Voting Fields: Direct Votes Computation and New Saliency Functions,
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IEEE DOI 0709
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Unsupervised Clustering using Multi-Resolution Perceptual Grouping,
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A Tensor Decomposition for Geometric Grouping and Segmentation,
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An Embodied Approach to Perceptual Grouping,
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IEEE DOI 0009
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Fish-Scales: Representing Fuzzy Manifolds,
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CVPR97(891-896).
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ICPR96(II: 462-466).
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Perceptual Organization, Figure Ground, Attention And Saliency,
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SCIA95(315-322). BibRef 9500

Wang, C.L., Prasanna, V.K., Chung, Y.,
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Preattentive Grouping and Attentive Selection for Early Visual Computation,
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Kang, H.B., Walker, E.L.,
Multilevel Grouping: Combining Bottom-Up and Top-Down Reasoning for Object Recognition,
ICPR94(A:559-562).
IEEE DOI BibRef 9400

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IEEE DOI BibRef 9000

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Chapter on Computational Vision, Regularization, Connectionist, Morphology, Scale-Space, Perceptual Grouping, Wavelets, Color, Sensors, Optical, Laser, Radar continues in
Perceptual Grouping, Saliency, Neural Networks, Learning .