8.4.1 Superpixel Region Extraction, Region Growing

Perbet, F.[Frank], Stenger, B.[Bjorn], Maki, A.[Atsuto],
Homogeneous Superpixels from Markov Random Walks,
IEICE(E95-D), No. 7, July 2012, pp. 1740-1748.
WWW Link. 1208
BibRef

Achanta, R.[Radhakrishna], Shaji, A.[Appu], Smith, K.[Kevin], Lucchi, A.[Aurelien], Fua, P.[Pascal], Süsstrunk, S.[Sabine],
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods,
PAMI(34), No. 11, November 2012, pp. 2274-2282.
IEEE DOI 1209
Look at 4 methods or image boundary, speed, efficience, segmentation performance. SLIC: Simple linear iterative clustering.
See also Are spatial and global constraints really necessary for segmentation?. For an implementation:
See also Bilateral K-Means for Superpixel Computation (the SLIC Method). BibRef

Achanta, R.[Radhakrishna], Süsstrunk, S.[Sabine],
Superpixels and Polygons Using Simple Non-iterative Clustering,
CVPR17(4895-4904)
IEEE DOI 1711
Clustering algorithms, Estimation, Image color analysis, Image segmentation, Iterative algorithms, Memory management, Partitioning, algorithms BibRef

Liu, B., Hu, H., Wang, H., Wang, K., Liu, X., Yu, W.,
Superpixel-Based Classification With an Adaptive Number of Classes for Polarimetric SAR Images,
GeoRS(51), No. 2, February 2013, pp. 907-924.
IEEE DOI 1302
BibRef

Liu, B., Zhang, Z., Liu, X., Yu, W.,
Representation and Spatially Adaptive Segmentation for PolSAR Images Based on Wedgelet Analysis,
GeoRS(53), No. 9, September 2015, pp. 4797-4809.
IEEE DOI 1506
Approximation methods BibRef

Tighe, J.[Joseph], Lazebnik, S.[Svetlana],
Superparsing,
IJCV(101), No. 2, January 2013, pp. 329-349.
WWW Link. 1302
BibRef
Earlier:
Understanding scenes on many levels,
ICCV11(335-342).
IEEE DOI 1201
BibRef
Earlier:
SuperParsing: Scalable Nonparametric Image Parsing with Superpixels,
ECCV10(V: 352-365).
Springer DOI 1009
Semantic labels in segmentation. Both basic level and detail level BibRef

Tighe, J.[Joseph], Lazebnik, S.[Svetlana],
Finding Things: Image Parsing with Regions and Per-Exemplar Detectors,
CVPR13(3001-3008)
IEEE DOI 1309
computer vision BibRef

Tighe, J.[Joseph], Niethammer, M.[Marc], Lazebnik, S.[Svetlana],
Scene Parsing with Object Instance Inference Using Regions and Per-exemplar Detectors,
IJCV(112), No. 2, April 2015, pp. 150-171.
Springer DOI 1504
BibRef
Earlier:
Scene Parsing with Object Instances and Occlusion Ordering,
CVPR14(3748-3755)
IEEE DOI 1409
BibRef

Wang, P.[Peng], Zeng, G.[Gang], Gan, R.[Rui], Wang, J.D.[Jing-Dong], Zha, H.B.[Hong-Bin],
Structure-Sensitive Superpixels via Geodesic Distance,
IJCV(103), No. 1, May 2013, pp. 1-21.
Springer DOI 1305
BibRef
Earlier: A2, A1, A4, A3, A5: ICCV11(447-454).
IEEE DOI 1201
BibRef

Liu, M.Y.[Ming-Yu], Tuzel, O.[Oncel], Ramalingam, S.[Srikumar], Chellappa, R.[Rama],
Entropy-Rate Clustering: Cluster Analysis via Maximizing a Submodular Function Subject to a Matroid Constraint,
PAMI(36), No. 1, 2014, pp. 99-112.
IEEE DOI 1312
BibRef
Earlier:
Entropy rate superpixel segmentation,
CVPR11(2097-2104).
IEEE DOI 1106
Clustering. BibRef

Vemulapalli, R., Tuzel, O.[Oncel], Liu, M.Y.[Ming-Yu],
Deep Gaussian Conditional Random Field Network: A Model-Based Deep Network for Discriminative Denoising,
CVPR16(4801-4809)
IEEE DOI 1612
BibRef

Vemulapalli, R., Tuzel, O.[Oncel], Liu, M.Y.[Ming-Yu], Chellappa, R.,
Gaussian Conditional Random Field Network for Semantic Segmentation,
CVPR16(3224-3233)
IEEE DOI 1612
BibRef

Xu, L.F.[Lin-Feng], Zeng, L.Y.[Liao-Yuan], Wang, Z.N.[Zheng-Ning],
Saliency-based superpixels,
SIViP(8), No. 1, January 2014, pp. 181-190.
Springer DOI 1402
BibRef

Tian, Z.Q.[Zhi-Qiang], Zheng, N.N.[Nan-Ning], Xue, J.R.[Jian-Ru], Lan, X.G.[Xu-Guang], Li, C.[Ce], Zhou, G.[Gang],
Video object segmentation with shape cue based on spatiotemporal superpixel neighbourhood,
IET-CV(8), No. 1, February 2014, pp. 16-25.
DOI Link 1404
image segmentation BibRef

Xie, Y.R.[Yu-Rui], Xu, L.F.[Ling-Feng], Wang, Z.N.[Zheng-Ning],
Automated co-superpixel generation via graph matching,
SIViP(8), No. 4, May 2014, pp. 753-763.
WWW Link. 1404
BibRef

Shen, J.B.[Jian-Bing], Du, Y.F.[Yun-Fan], Wang, W.G.[Wen-Guan], Li, X.L.[Xue-Long],
Lazy Random Walks for Superpixel Segmentation,
IP(23), No. 4, April 2014, pp. 1451-1462.
IEEE DOI 1404
image segmentation BibRef

Peng, J.T.[Jian-Teng], Shen, J.B.[Jian-Bing], Li, X.L.[Xue-Long],
High-Order Energies for Stereo Segmentation,
Cyber(46), No. 7, July 2016, pp. 1616-1627.
IEEE DOI 1606
Computer vision BibRef

Dong, X.P.[Xing-Ping], Shen, J.B.[Jian-Bing], Shao, L., Van Gool, L.J.[Luc J.],
Sub-Markov Random Walk for Image Segmentation,
IP(25), No. 2, February 2016, pp. 516-527.
IEEE DOI 1601
BibRef
Earlier: A1, A2, A4, Only:
Segmentation Using SubMarkov Random Walk,
EMMCVPR15(237-248).
Springer DOI 1504
Algorithm design and analysis BibRef

Schick, A.[Alexander], Fischer, M.[Mika], Stiefelhagen, R.[Rainer],
An evaluation of the compactness of superpixels,
PRL(43), No. 1, 2014, pp. 71-80.
Elsevier DOI 1404
BibRef
Earlier:
Measuring and evaluating the compactness of superpixels,
ICPR12(930-934).
WWW Link. 1302
Award, ICPR. BibRef
Earlier: A1, A3, Only:
Evaluating image segments by applying the description length to sets of superpixels,
ITCVPR11(1394-1401).
IEEE DOI 1201
Superpixel segmentation BibRef

Fu, H., Cao, X., Tang, D., Han, Y., Xu, D.,
Regularity Preserved Superpixels and Supervoxels,
MultMed(16), No. 4, June 2014, pp. 1165-1175.
IEEE DOI 1407
Accuracy BibRef

Morerio, P., Georgiu, G.C., Marcenaro, L., Regazzoni, C.S.,
Optimizing Superpixel Clustering for Real-Time Egocentric-Vision Applications,
SPLetters(22), No. 4, April 2015, pp. 469-473.
IEEE DOI 1411
belief networks BibRef

Fan, Q.A.[Qi-Ang], Qi, C.[Chun],
Two-stage salient region detection by exploiting multiple priors,
JVCIR(25), No. 8, 2014, pp. 1823-1834.
Elsevier DOI 1411
Superpixel isolation BibRef

Zhu, L.[Lei], Klein, D.A., Frintrop, S., Cao, Z.G.[Zhi-Guo], Cremers, A.B.,
A Multisize Superpixel Approach for Salient Object Detection Based on Multivariate Normal Distribution Estimation,
IP(23), No. 12, December 2014, pp. 5094-5107.
IEEE DOI 1412
normal distribution BibRef

Buyssens, P.[Pierre], Gardin, I.[Isabelle], Ruan, S.[Su], El Moataz, A.[Abderrahim],
Eikonal-based region growing for efficient clustering,
IVC(32), No. 12, 2014, pp. 1045-1054.
Elsevier DOI 1412
Superpixels BibRef

Buyssens, P.[Pierre], Lezoray, O.[Olivier],
Multivalued label diffusion for semi-supervised segmentation,
ICIP15(3275-3279)
IEEE DOI 1512
Diffusion BibRef

Buyssens, P., Toutain, M., El Moataz, A., Lezoray, O.,
Eikonal-based vertices growing and iterative seeding for efficient graph-based segmentation,
ICIP14(4368-4372)
IEEE DOI 1502
Clustering algorithms BibRef

Tian, X.L.[Xiao-Lin], Jiao, L.C.[Li-Cheng], Yi, L.[Long], Guo, K.[Kaiwu], Zhang, X.H.[Xiao-Hua],
The image segmentation based on optimized spatial feature of superpixel,
JVCIR(26), No. 1, 2015, pp. 146-160.
Elsevier DOI 1502
Image segmentation BibRef

Tian, X.L.[Xiao-Lin], Jiao, L.C.[Li-Cheng], Zheng, X.L.[Xiao-Li], Zhang, X.H.[Xiao-Hua],
Inter-frame constrained coding based on superpixel for tracking,
VC(31), No. 5, May 2015, pp. 701-715.
WWW Link. 1505
BibRef

van den Bergh, M.[Michael], Boix, X.[Xavier], Roig, G.[Gemma], Van Gool, L.J.[Luc J.],
SEEDS: Superpixels Extracted Via Energy-Driven Sampling,
IJCV(111), No. 3, February 2015, pp. 298-314.
Springer DOI 1503
BibRef
Earlier: Insert A4: de Capitani, B.[Benjamin], ECCV12(VII: 13-26).
Springer DOI 1210
BibRef

van den Bergh, M.[Michael], Roig, G.[Gemma], Boix, X.[Xavier], Manen, S.[Santiago], Van Gool, L.J.[Luc J.],
Online Video SEEDS for Temporal Window Objectness,
ICCV13(377-384)
IEEE DOI 1403
Super pixels. BibRef

Tasli, H.E.[H. Emrah], Cigla, C.[Cevahir], Alatan, A.A.[A. Aydin],
Convexity constrained efficient superpixel and supervoxel extraction,
SP:IC(33), No. 1, 2015, pp. 71-85.
Elsevier DOI 1504
Superpixel BibRef

Machairas, V.[Vaia], Faessel, M., Cardenas-Pena, D., Chabardes, T., Walter, T., Décencière, E.[Etienne],
Waterpixels,
IP(24), No. 11, November 2015, pp. 3707-3716.
IEEE DOI 1509
BibRef
Earlier: A1, A6, A5, Only:
Waterpixels: Superpixels based on the watershed transformation,
ICIP14(4343-4347)
IEEE DOI 1502
image segmentation. Art
See also Watervoxels. BibRef

Cettour-Janet, P.[Pierre], Cazorla, C.[Clément], Machairas, V.[Vaia], Delannoy, Q.[Quentin], Bednarek, N.[Nathalie], Rousseau, F.[François], Décencière, E.[Etienne], Passat, N.[Nicolas],
Watervoxels,
IPOL(9), 2019, pp. 317-328.
DOI Link 1911
Code, Segmentation. Voxels, derived from waterpixels which were drived from superpixels.
See also Waterpixels. BibRef

Tasli, H.E.[H. Emrah], Sicre, R.[Ronan], Gevers, T.[Theo],
SuperPixel based mid-level image description for image recognition,
JVCIR(33), No. 1, 2015, pp. 301-308.
Elsevier DOI 1512
BibRef
Earlier: A2, A1, A3:
SuperPixel Based Angular Differences as a Mid-level Image Descriptor,
ICPR14(3732-3737)
IEEE DOI 1412
Color BibRef

Sicre, R.[Ronan], Jurie, F.[Frédéric],
Discriminative part model for visual recognition,
CVIU(141), No. 1, 2015, pp. 28-37.
Elsevier DOI 1512
BibRef
Earlier:
Discovering and Aligning Discriminative Mid-level Features for Image Classification,
ICPR14(1975-1980)
IEEE DOI 1412
Boats BibRef

Saranathan, A.M., Parente, M.,
Uniformity-Based Superpixel Segmentation of Hyperspectral Images,
GeoRS(54), No. 3, March 2016, pp. 1419-1430.
IEEE DOI 1603
Approximation methods BibRef

Wang, X.[Xiang], Ma, H.M.[Hui-Min], Chen, X.Z.[Xiao-Zhi],
Geodesic weighted Bayesian model for saliency optimization,
PRL(75), No. 1, 2016, pp. 1-8.
Elsevier DOI 1604
BibRef
And:
Salient object detection via fast R-CNN and low-level cues,
ICIP16(1042-1046)
IEEE DOI 1610
BibRef
Earlier:
Geodesic weighted Bayesian model for salient object detection,
ICIP15(397-401)
IEEE DOI 1512
Bayesian framework; Salient object detection; geodesic weight; superpixel BibRef

Wang, X.[Xiang], Ma, H.M.[Hui-Min], Chen, X.Z.[Xiao-Zhi], You, S.,
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection,
IP(27), No. 1, January 2018, pp. 121-134.
IEEE DOI 1712
edge detection, image colour analysis, neural nets, object detection, CNN based methods, RGB-D saliency detection, object mask BibRef

Choi, K.S.[Kang-Sun], Oh, K.W.[Ki-Won],
Subsampling-based acceleration of simple linear iterative clustering for superpixel segmentation,
CVIU(146), No. 1, 2016, pp. 1-8.
Elsevier DOI 1604
Superpixels BibRef

Peng, J., Shen, J., Yao, A., Li, X.,
Superpixel Optimization Using Higher Order Energy,
CirSysVideo(26), No. 5, May 2016, pp. 917-927.
IEEE DOI 1605
Clustering algorithms BibRef

Shen, J., Hao, X., Liang, Z., Liu, Y., Wang, W., Shao, L.,
Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm,
IP(25), No. 12, December 2016, pp. 5933-5942.
IEEE DOI 1612
distance measurement BibRef

Zhang, Y.X.[Yong-Xia], Ma, L.[Long], Zhou, Y.F.[Yuan-Feng], Zhang, C.M.[Cai-Ming],
Automatic superpixel generation algorithm based on a quadric error metric in 3D space,
SIViP(11), No. 3, March 2017, pp. 471-478.
Springer DOI 1702
BibRef

Csillik, O.[Ovidiu],
Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Wang, M.[Murong], Liu, X.B.[Xia-Bi], Gao, Y.X.[Yi-Xuan], Ma, X.[Xiao], Soomro, N.Q.[Nouman Q.],
Superpixel segmentation: A benchmark,
SP:IC(56), No. 1, 2017, pp. 28-39.
Elsevier DOI 1706
Survey, Superpixel Segmentation. Superpixel BibRef

Zhang, Y., Li, X., Gao, X., Zhang, C.,
A Simple Algorithm of Superpixel Segmentation With Boundary Constraint,
CirSysVideo(27), No. 7, July 2017, pp. 1502-1514.
IEEE DOI 1707
Algorithm design and analysis, Clustering algorithms, Complexity theory, Distance measurement, Image edge detection, Image segmentation, Shape, Image preprocessing, image segmentation, oversegmentation, superpixel BibRef

Guo, Y., Jia, X., Paull, D.,
Superpixel-Based Adaptive Kernel Selection for Angular Effect Normalization of Remote Sensing Images With Kernel Learning,
GeoRS(55), No. 8, August 2017, pp. 4262-4271.
IEEE DOI 1708
Dictionaries, Image segmentation, Kernel, Land surface, Remote sensing, Satellites, Scattering, Adaptive kernel selection, bidirectional reflectance, image normalization, kernel, learning BibRef

Guo, Y., Jia, X., Paull, D.,
Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification,
IP(27), No. 6, June 2018, pp. 3036-3048.
IEEE DOI 1804
Australia, Data mining, Market research, Remote sensing, Sensors, Support vector machines, Training, Multitemporal, classification, support vector machines BibRef

Zhang, Q.A.[Qi-Ang], Liu, Y.[Yi], Zhu, S.Y.[Si-Yang], Han, J.G.[Jun-Gong],
Salient object detection based on super-pixel clustering and unified low-rank representation,
CVIU(161), No. 1, 2017, pp. 51-64.
Elsevier DOI 1708
Salient object detection. BibRef

Wang, X.Y.[Xuan-Yin], Wu, C.W.[Chang-Wei], Xiang, K.[Ke], Chen, W.[Wen],
Efficient local and global contour detection based on superpixels,
JVCIR(48), No. 1, 2017, pp. 77-87.
Elsevier DOI 1708
Contour detection BibRef

Wang, X.Y.[Xuan-Yin], Wu, C.W.[Chang-Wei], Xiang, K.[Ke], Xiang, S.W.[Sen-Wei], Chen, W.[Wen],
An experimental comparison of superpixels detection methods for contour detection,
MVA(29), No. 4, May 2018, pp. 677-687.
Springer DOI
WWW Link. 1805
BibRef

Yang, J.F.[Jin-Fu], Wang, Y.[Ying], Wang, G.H.[Guang-Hui], Li, M.G.[Min-Gai],
Salient Object Detection Based on Global Multi-Scale Superpixel Contrast,
IET-CV(11), No. 8, December 2017, pp. 710-716.
DOI Link 1712
BibRef

Wu, S., Nakao, M., Matsuda, T.,
SuperCut: Superpixel Based Foreground Extraction With Loose Bounding Boxes in One Cutting,
SPLetters(24), No. 12, December 2017, pp. 1803-1807.
IEEE DOI 1712
Haar transforms, feature extraction, image segmentation, interactive systems, wavelet transforms, Haar-wavelet feature, interactive image segmentation BibRef

Zhou, Y., Pan, X., Wang, W., Yin, Y., Zhang, C.,
Superpixels by Bilateral Geodesic Distance,
CirSysVideo(27), No. 11, November 2017, pp. 2281-2293.
IEEE DOI 1712
Clustering algorithms, Computer science, Image color analysis, Image segmentation, Level measurement, superpixel BibRef

Linares, O.A.C.[Oscar A.C.], Botelho, G.M.[Glenda Michele], Rodrigues, F.A.[Francisco Aparecido], Santo Batista Neto, J.D.[João Do_Espirito],
Segmentation of large images based on super-pixels and community detection in graphs,
IET-IPR(11), No. 12, Decmeber 2017, pp. 1219-1228.
DOI Link 1712
BibRef

Belizario, I.V.[Ivar Vargas], Linares, O.C.[Oscar Cuadros], Santo Batista Neto, J.D.[João Do_Espirito],
Automatic image segmentation based on label propagation,
IET-IPR(15), No. 11, 2021, pp. 2532-2547.
DOI Link 2108
BibRef

Stutz, D.[David], Hermans, A.[Alexander], Leibe, B.[Bastian],
Superpixels: An evaluation of the state-of-the-art,
CVIU(166), No. 1, 2018, pp. 1-27.
Elsevier DOI 1712
Survey, Superpixels. Superpixels BibRef

Gong, Y.J., Zhou, Y.,
Differential Evolutionary Superpixel Segmentation,
IP(27), No. 3, March 2018, pp. 1390-1404.
IEEE DOI 1801
Algorithm design and analysis, Clustering algorithms, Computational complexity, Image segmentation, Optimization, seeding BibRef

Liu, Y.J., Yu, M.J., Li, B.J., He, Y.,
Intrinsic Manifold SLIC: A Simple and Efficient Method for Computing Content-Sensitive Superpixels,
PAMI(40), No. 3, March 2018, pp. 653-666.
IEEE DOI 1802
Clustering algorithms, Euclidean distance, Image color analysis, Image segmentation, Iterative methods, Manifolds, Time complexity, image segmentation BibRef

Liu, Y.J., Yu, C.C., Yu, M.J., He, Y.,
Manifold SLIC: A Fast Method to Compute Content-Sensitive Superpixels,
CVPR16(651-659)
IEEE DOI 1612
BibRef

Sima, H., Guo, P., Zou, Y., Wang, Z., Xu, M.,
Bottom-Up Merging Segmentation for Color Images With Complex Areas,
SMCS(48), No. 3, March 2018, pp. 354-365.
IEEE DOI 1802
Color, Computational modeling, Feature extraction, Image color analysis, Image segmentation, Merging, Tensile stress, superpixels BibRef

Ban, Z.H.[Zhi-Hua], Liu, J.G.[Jian-Guo], Fouriaux, J.[Jeremy],
GLSC: LSC superpixels at over 130 FPS,
RealTimeIP(14), No. 3, March 2018, pp. 605-616.
WWW Link. 1804
BibRef

Meng, F., Li, H., Wu, Q., Luo, B., Huang, C., Ngan, K.N.,
Globally Measuring the Similarity of Superpixels by Binary Edge Maps for Superpixel Clustering,
CirSysVideo(28), No. 4, April 2018, pp. 906-919.
IEEE DOI 1804
Distribution functions, Graphical models, Image color analysis, Image edge detection, Image segmentation, global similarity measurement BibRef

Xiao, X., Zhou, Y., Gong, Y.J.,
Content-Adaptive Superpixel Segmentation,
IP(27), No. 6, June 2018, pp. 2883-2896.
IEEE DOI 1804
image resolution, image segmentation, image texture, iterative methods, CAS, discriminability measure, superpixel BibRef

Xu, L.[Li], Luo, B.[Bing], Pei, Z.[Zheng],
Weak Boundary Preserved Superpixel Segmentation Based on Directed Graph Clustering,
SP:IC(65), 2018, pp. 231-239.
Elsevier DOI 1805
Superpixel segmentation, Directed graph clustering, K-NN graph, Integer programming BibRef

Ibrahim, A.[Abdelhameed], Tharwat, A.[Alaa], Gaber, T.[Tarek], Hassanien, A.E.[Aboul Ella],
Optimized superpixel and AdaBoost classifier for human thermal face recognition,
SIViP(12), No. 4, May 2018, pp. 711-719.
WWW Link. 1805
BibRef

Zhao, Q., Dai, F., Ma, Y., Wan, L., Zhang, J., Zhang, Y.,
Spherical Superpixel Segmentation,
MultMed(20), No. 6, June 2018, pp. 1406-1417.
IEEE DOI 1805
Algorithm design and analysis, Clustering algorithms, Geometry, Image color analysis, Image segmentation, Shape, spherical image BibRef

Ban, Z., Liu, J., Cao, L.,
Superpixel Segmentation Using Gaussian Mixture Model,
IP(27), No. 8, August 2018, pp. 4105-4117.
IEEE DOI 1806
Computational complexity, Covariance matrices, Erbium, Feature extraction, Image color analysis, Image segmentation, parallel algorithms BibRef

Giraud, R.[Rémi], Ta, V.T.[Vinh-Thong], Papadakis, N.[Nicolas],
Robust superpixels using color and contour features along linear path,
CVIU(170), 2018, pp. 1-13.
Elsevier DOI 1806
BibRef
Earlier:
SCALP: Superpixels with Contour Adherence using Linear Path,
ICPR16(2374-2379)
IEEE DOI 1705
Superpixels, Linear path, Segmentation, Contour detection. Clustering algorithms, Image color analysis, Image segmentation, Measurement, Scalp, Shape, Standards BibRef

Sultani, W., Mokhtari, S., Yun, H.B.,
Automatic Pavement Object Detection Using Superpixel Segmentation Combined With Conditional Random Field,
ITS(19), No. 7, July 2018, pp. 2076-2085.
IEEE DOI 1807
Feature extraction, Histograms, Image segmentation, Object detection, Shape, Support vector machines, superpixel segmentation BibRef

Huang, C., Wang, W., Wang, W., Lin, S., Lin, Y.,
USEAQ: Ultra-Fast Superpixel Extraction via Adaptive Sampling From Quantized Regions,
IP(27), No. 10, October 2018, pp. 4916-4931.
IEEE DOI 1808
feature extraction, image colour analysis, image representation, image segmentation, sampling methods, joint spatial and color quantizations BibRef

Wei, X., Yang, Q., Gong, Y., Ahuja, N., Yang, M.,
Superpixel Hierarchy,
IP(27), No. 10, October 2018, pp. 4838-4849.
IEEE DOI 1808
edge detection, image segmentation, superpixel segmentation hierarchy, Boruvka algorithm BibRef

Fu, Z.L.[Zhong-Liang], Sun, Y.J.[Yang-Jie], Fan, L.[Liang], Han, Y.T.[Yu-Tao],
Multiscale and Multifeature Segmentation of High-Spatial Resolution Remote Sensing Images Using Superpixels with Mutual Optimal Strategy,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
BibRef

Cong, L.[Lin], Ding, S.F.[Shi-Fei], Wang, L.J.[Li-Juan], Zhang, A.J.[Ai-Juan], Jia, W.K.[Wei-Kuan],
Image segmentation algorithm based on superpixel clustering,
IET-IPR(12), No. 11, November 2018, pp. 2030-2035.
DOI Link 1810
BibRef
And: Corrigendum: IET-IPR(13), No. 11, 19 September 2019, pp. 2029-2029.
DOI Link 1909
BibRef

Ding, S.F.[Shi-Fei], Wang, L.J.[Li-Juan], Cong, L.[Lin],
Super-pixel image segmentation algorithm based on adaptive equalisation feature parameters,
IET-IPR(14), No. 17, 24 December 2020, pp. 4461-4467.
DOI Link 2104
BibRef

Fan, J.Y.[Jia-Yuan], Chen, T.[Tao], Lu, S.J.[Shi-Jian],
Superpixel Guided Deep-Sparse-Representation Learning for Hyperspectral Image Classification,
CirSysVideo(28), No. 11, November 2018, pp. 3163-3173.
IEEE DOI 1811
Feature extraction, Encoding, Image segmentation, Principal component analysis, Support vector machines, sparse representation BibRef

Mendonça, M.[Marcelo], Oliveira, L.[Luciano],
ISEC: Iterative over-segmentation via edge clustering,
IVC(80), 2018, pp. 45-57.
Elsevier DOI 1812
Superpixels, Video object segmentation BibRef

Liu, H.[Han], Li, J.[Jun], He, L.[Lin], Wang, Y.[Yu],
Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Li, W.M.[Wen-Mei], Chen, H.H.[Huai-Huai], Liu, Q.[Qing], Liu, H.Y.[Hai-Yan], Wang, Y.[Yu], Gui, G.[Guan],
Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Ma, F.[Fei], Gao, F.[Fei], Sun, J.P.[Jin-Ping], Zhou, H.Y.[Hui-Yu], Hussain, A.[Amir],
Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Ma, F.[Fei], Gao, F.[Fei], Sun, J.P.[Jin-Ping], Zhou, H.Y.[Hui-Yu], Hussain, A.[Amir],
Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Wang, W.[Wei], Xiang, D.L.[De-Liang], Ban, Y.F.[Yi-Fang], Zhang, J.[Jun], Wan, J.W.[Jian-Wei],
Superpixel-Based Segmentation of Polarimetric SAR Images through Two-Stage Merging,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Xu, L.[Li], Luo, B.[Bing], Kong, M.M.[Ming-Ming], Li, B.[Bo], Pei, Z.[Zheng],
Fast Superpixel Segmentation via Boundary Sampling and Interpolation,
IEICE(E102-D), No. 4, April 2019, pp. 871-874.
WWW Link. 1904
BibRef

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greedy algorithms, image colour analysis, image resolution, image texture, neural nets, optimisation, neural network BibRef

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Image segmentation, Image color analysis, Shape, Merging, Clustering algorithms, Entropy, Lattices, superpixel BibRef

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Xiang, D.L.[De-Liang], Wang, W.[Wei], Tang, T.[Tao], Guan, D.D.[Dong-Dong], Quan, S.N.[Si-Nong], Liu, T.[Tao], Su, Y.[Yi],
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Edge penalty, homogeneity measurement (HoM), image segmentation, polarimetric synthetic aperture radar (PolSAR), superpixel merging BibRef

Xiang, D.L.[De-Liang], Zhang, F.[Fan], Zhang, W.[Wei], Tang, T.[Tao], Guan, D.D.[Dong-Dong], Zhang, L.[Liang], Su, Y.[Yi],
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IEEE DOI 2111
Image edge detection, Radar polarimetry, Merging, Image segmentation, Detectors, Speckle, Feature extraction, synthetic aperture radar (SAR) image segmentation BibRef

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Edge detection, Polygonal meshes, Persistent homology BibRef

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Tensile stress, Transforms, Image reconstruction, Image coding, Sensors, Computational modeling, Gray-scale, Sparse representation, spectral imaging BibRef

Liu, L., Wang, Y., Peng, J., Zhang, L., Zhang, B., Cao, Y.,
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Feature learning, hyperspectral image (HSI) classification, latent relationship, superpixels constraint BibRef

Zhao, Y.F.[Yi-Fei], Su, F.Z.[Fen-Zhen], Yan, F.Q.[Feng-Qin],
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Streaming media, Motion segmentation, Image segmentation, Labeling, Task analysis, Metadata, Probabilistic logic, Video segmentation, Markov random field BibRef

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Image foresting transform, Superpixels, Differential image foresting transform BibRef

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Image segmentation, Superpixel, Deep learning BibRef

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Ma, D., Zhou, Y., Xin, S., Wang, W.,
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IP(30), 2021, pp. 1825-1839.
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Image segmentation, Shape, Image edge detection, Clustering algorithms, Image color analysis, Image coding, Erbium, optimization BibRef

Zhang, J.C.[Jian-Chao], Aviles-Rivero, A.I.[Angelica I.], Heydecker, D.[Daniel], Zhuang, X.S.[Xiao-Sheng], Chan, R.[Raymond], Schönlieb, C.B.[Carola-Bibiane],
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Elsevier DOI 2102
Superpixels, K-means, Spectral residual, Segmentation BibRef

Peng, H.K.[Han-Kui], Aviles-Rivero, A.I.[Angelica I.], Schönlieb, C.B.[Carola-Bibiane],
HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation,
WACV22(72-81)
IEEE DOI 2202
Deep learning, Visualization, Entropy, Real-time systems, Feeds, Task analysis, Image Processing BibRef

Ji, S.[Sifan], Zhu, H.Q.[Hong-Qing], Wang, P.Y.[Peng-Yu], Ling, X.F.[Xiao-Feng],
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DOI Link 2103
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Liu, C.X.[Cai-Xia], Pang, M.Y.[Ming-Yong], Zhao, R.B.[Rui-Bin],
Novel superpixel-based algorithm for segmenting lung images via convolutional neural network and random forest,
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DOI Link 2103
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Li, C.[Cheng], Guo, B.L.[Bao-Long], Liao, N.N.[Nan-Nan], Gong, J.L.[Jiang-Lei], Han, X.D.[Xiao-Dong], Hou, S.W.[Shu-Wei], Chen, Z.J.[Zhi-Jie], He, W.P.[Wang-Peng],
CONIC: Contour Optimized Non-Iterative Clustering Superpixel Segmentation,
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Zhang, Y.X.[Yong-Xia], Guo, Q.[Qiang], Zhang, Y.S.[Yong-Sheng], Zhang, C.M.[Cai-Ming],
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IET-IPR(14), No. 17, 24 December 2020, pp. 4543-4553.
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Song, R.[Rui], Sun, W.W.[Wei-Wei], Du, Q.[Qian],
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GeoRS(59), No. 6, June 2021, pp. 5114-5130.
IEEE DOI 2106
Feature extraction, Kernel, Training, Hyperspectral imaging, Task analysis, Nonhomogeneous media, superpixel segmentation BibRef

Mukherjee, A.[Aritra], Sarkar, S.[Soumik], Saha, S.K.[Sanjoy K.],
Segmentation of natural images based on super pixel and graph merging,
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Wilms, C.[Christian], Frintrop, S.[Simone],
DeepFH segmentations for superpixel-based object proposal refinement,
IVC(114), 2021, pp. 104263.
Elsevier DOI 2109
BibRef
Earlier:
Superpixel-based Refinement for Object Proposal Generation,
ICPR21(4965-4972)
IEEE DOI 2105
Object proposals, Image segmentation, Superpixels. Image segmentation, Statistical analysis, Measurement uncertainty, Object segmentation, Feature extraction, Proposals BibRef

Yuan, Y.[Ye], Zhang, W.[Wei], Yu, H.[Hai], Zhu, Z.L.[Zhi-Liang],
Superpixels With Content-Adaptive Criteria,
IP(30), 2021, pp. 7702-7716.
IEEE DOI 2109
Image color analysis, Shape, Image segmentation, Clustering algorithms, Visualization, Topology, Shape measurement, boundary refinement BibRef

Li, D.[Dan], Kong, F.Q.[Fang-Qiang], Liu, J.H.[Jia-Hang], Wang, Q.[Qiang],
Superpixel-Based Multiple Statistical Feature Extraction Method for Classification of Hyperspectral Images,
GeoRS(59), No. 10, October 2021, pp. 8738-8753.
IEEE DOI 2109
Feature extraction, Training, Kernel, Data mining, Manifolds, Covariance descriptor, sparse representation (SR) classifier BibRef

Chuchvara, A.[Aleksandra], Gotchev, A.[Atanas],
Efficient Image-Warping Framework for Content-Adaptive Superpixels Generation,
SPLetters(28), 2021, pp. 1948-1952.
IEEE DOI 2110
Image segmentation, Optimization, Transforms, Signal processing algorithms, Image edge detection, GPU BibRef

Yin, J.J.[Jun-Jun], Wang, T.[Tao], Du, Y.L.[Yan-Lei], Liu, X.[Xiyun], Zhou, L.J.[Liang-Jiang], Yang, J.[Jian],
SLIC Superpixel Segmentation for Polarimetric SAR Images,
GeoRS(60), 2022, pp. 1-17.
IEEE DOI 2112
Radar polarimetry, Image segmentation, Synthetic aperture radar, Clustering algorithms, Scattering, Iterative methods, synthetic aperture radar (SAR) BibRef

Wang, N.N.[Nan-Nan], Zhang, Y.X.[Yong-Xia],
Adaptive and fast image superpixel segmentation approach,
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Elsevier DOI 2112
Image processing, Superpixels, Linear path, LBP, Contour density BibRef

Zhao, C.H.[Chun-Hui], Qin, B.[Boao], Feng, S.[Shou], Zhu, W.X.[Wen-Xiang],
Multiple Superpixel Graphs Learning Based on Adaptive Multiscale Segmentation for Hyperspectral Image Classification,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
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Zhao, C.H.[Chun-Hui], Qin, B.[Boao], Feng, S.[Shou], Zhu, W.X.[Wen-Xiang], Sun, W.W.[Wei-Wei], Li, W.[Wei], Jia, X.P.[Xiu-Ping],
Hyperspectral Image Classification with Multi-Attention Transformer and Adaptive Superpixel Segmentation-Based Active Learning,
IP(32), 2023, pp. 3606-3621.
IEEE DOI 2307
Transformers, Training, Feature extraction, Hyperspectral imaging, Convolutional neural networks, Task analysis, Convolution, adoptive superpixel segmentation BibRef

Liu, J.F.[Jia-Fei], Wang, Q.S.[Qing-Song], Cheng, J.[Jianda], Xiang, D.L.[De-Liang], Jing, W.B.[Wen-Bo],
Multitask Learning-Based for SAR Image Superpixel Generation,
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DOI Link 2202
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Yi, S.[Sheng], Ma, H.M.[Hui-Min], Wang, X.[Xiang], Hu, T.Y.[Tian-Yu], Li, X.[Xi], Wang, Y.[Yu],
Weakly-supervised semantic segmentation with superpixel guided local and global consistency,
PR(124), 2022, pp. 108504.
Elsevier DOI 2203
Weakly supervised condition, Semantic segmentation, Pixel-level affinity, Superpixel BibRef

Yang, X.[Xuan], Chen, Z.C.[Zheng-Chao], Zhang, B.[Bing], Li, B.P.[Bai-Peng], Bai, Y.Q.[Yong-Qing], Chen, P.[Pan],
A Block Shuffle Network with Superpixel Optimization for Landsat Image Semantic Segmentation,
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DOI Link 2204
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Liu, T.[Tianli], Dai, F.[Fang], Guo, W.[Wenyan], Zhao, F.Q.[Feng-Qun], Wang, J.F.[Jun-Feng], Wang, X.X.[Xiao-Xia],
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DOI Link 2205
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Mu, C.H.[Cai-Hong], Dong, Z.D.[Zhi-Dong], Liu, Y.[Yi],
A Two-Branch Convolutional Neural Network Based on Multi-Spectral Entropy Rate Superpixel Segmentation for Hyperspectral Image Classification,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
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Gay, R.[Robin], Lecoutre, J.[Jérémie], Menouret, N.[Nicolas], Morillon, A.[Arthur], Monasse, P.[Pascal],
Bilateral K-Means for Superpixel Computation (the SLIC Method),
IPOL(12), 2022, pp. 72-91.
DOI Link 2205
Code, Superpixel. Code, SLIC. SLIC: Simple linear iterative clustering.
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Yan, T.M.[Ting-Man], Huang, X.L.[Xiao-Lin], Zhao, Q.F.[Qun-Fei],
Hierarchical Superpixel Segmentation by Parallel CRTrees Labeling,
IP(31), 2022, pp. 4719-4732.
IEEE DOI 2207
Labeling, Forestry, Image segmentation, Graphics processing units, Clustering algorithms, Prediction algorithms, Vegetation, parallel algorithm BibRef

Ouyang, C.[Cheng], Biffi, C.[Carlo], Chen, C.[Chen], Kart, T.[Turkay], Qiu, H.Q.[Hua-Qi], Rueckert, D.[Daniel],
Self-Supervised Learning for Few-Shot Medical Image Segmentation,
MedImg(41), No. 7, July 2022, pp. 1837-1848.
IEEE DOI 2207
BibRef
Earlier:
Self-supervision with Superpixels: Training Few-shot Medical Image Segmentation Without Annotation,
ECCV20(XXIX: 762-780).
Springer DOI 2010
Image segmentation, Biomedical imaging, Training, Prototypes, Task analysis, Semantics, Annotations, Self-supervised learning, representation learning. BibRef

Pan, X.[Xiao], Zhou, Y.F.[Yuan-Feng], Zhang, Y.F.[Yun-Feng], Zhang, C.M.[Cai-Ming],
Fast Generation of Superpixels With Lattice Topology,
IP(31), 2022, pp. 4828-4841.
IEEE DOI 2208
Lattices, Topology, Clustering algorithms, Image segmentation, Partitioning algorithms, Task analysis, Deep learning, Superpixels, deep learning BibRef

Liao, N.N.[Nan-Nan], Guo, B.[Baolong], Li, C.[Cheng], Liu, H.[Hui], Zhang, C.Y.[Chao-Yan],
BACA: Superpixel Segmentation with Boundary Awareness and Content Adaptation,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
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Deng, J.H.[Jie-Hang], Chen, H.M.[Hao-Min], Yuan, Z.M.[Zhong-Ming], Gu, G.S.[Guo-Sheng], Xu, S.[Shihe], Weng, S.W.[Shao-Wei], Wang, H.[Hao],
An enhanced image quality assessment by synergizing superpixels and visual saliency,
JVCIR(88), 2022, pp. 103610.
Elsevier DOI 2210
Full reference, Image quality assessment, Visual saliency, Superpixel segmentation, Limitations, Complementary BibRef

Deng, J.[Jie], Wang, W.[Wei], Quan, S.N.[Si-Nong], Zhan, R.H.[Rong-Hui], Zhang, J.[Jun],
Hierarchical Superpixel Segmentation for PolSAR Images Based on the Boruvka Algorithm,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Ye, P.J.[Pan-Jian], Han, C.H.[Chen-Hua], Zhang, Q.Z.[Qi-Zhong], Gao, F.R.[Fa-Rong], Yang, Z.Y.[Zhang-Yi], Wu, G.H.[Guang-Hai],
An Application of Hyperspectral Image Clustering Based on Texture-Aware Superpixel Technique in Deep Sea,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
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Liu, Q.[Qiang], Lu, X.[Xiao], Dong, Q.[Qiulei], Zhang, Y.Y.[Yang-Yong], Wang, H.X.[Hai-Xia],
SG-SRNs: Superpixel-Guided Scene Representation Networks,
SPLetters(29), 2022, pp. 2038-2042.
IEEE DOI 2210
Image segmentation, Task analysis, Image color analysis, Image reconstruction, Distortion, Scene representation networks, superpixel regularization BibRef

Yu, H.[Hang], Jiang, H.R.[Hao-Ran], Liu, Z.H.[Zhi-Heng], Zhou, S.P.[Sui-Ping], Yin, X.J.[Xiang-Jie],
EDTRS: A Superpixel Generation Method for SAR Images Segmentation Based on Edge Detection and Texture Region Selection,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Ng, T.C.[Tsz Ching], Choy, S.K.[Siu Kai], Lam, S.Y.[Shu Yan], Yu, K.W.[Kwok Wai],
Fuzzy Superpixel-based Image Segmentation,
PR(134), 2023, pp. 109045.
Elsevier DOI 2212
Fuzzy algorithm, Graph theory, Mean-shift, Segmentation, Superpixel BibRef

Zhang, Y.S.[Yong-Sheng], Zhang, Y.X.[Yong-Xia], Fan, L.W.[Lin-Wei], Wang, N.N.[Nan-Nan],
Fast and accurate superpixel segmentation algorithm with a guidance image,
IVC(129), 2023, pp. 104596.
Elsevier DOI 2301
Image segmentation, Superpixel, Real-time, Guidance image, Accurate BibRef

Zhou, P.[Pei], Kang, X.J.[Xue-Jing], Ming, A.[Anlong],
Vine Spread for Superpixel Segmentation,
IP(32), 2023, pp. 878-891.
IEEE DOI 2301
Image segmentation, Image color analysis, Shape, Soil, Feature extraction, Task analysis, Physiology, vine spread BibRef

Li, M.[Meilin], Zou, H.X.[Huan-Xin], Qin, X.X.[Xian-Xiang], Dong, Z.[Zhen], Sun, L.[Li], Wei, J.[Juan],
Superpixel Generation for Polarimetric SAR Images with Adaptive Size Estimation and Determinant Ratio Test Distance,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Xu, Y.Y.[Yun-Yang], Gao, X.F.[Xi-Feng], Zhang, C.M.[Cai-Ming], Tan, J.C.[Jian-Chao], Li, X.M.[Xue-Mei],
High Quality Superpixel Generation Through Regional Decomposition,
CirSysVideo(33), No. 4, April 2023, pp. 1802-1815.
IEEE DOI 2304
Image segmentation, Image edge detection, Image color analysis, Shape, Clustering algorithms, Task analysis, Merging, saliency detection BibRef

Dornaika, F., Sun, D., Hammoudi, K., Charafeddine, J., Cabani, A., Zhang, C.,
Object-centric Contour-aware Data Augmentation Using Superpixels of Varying Granularity,
PR(139), 2023, pp. 109481.
Elsevier DOI 2304
Data augmentation, Cutmix, Object-centric Contour-aware, Discriminative regions, Attention, Superpixels BibRef

Sun, L.M.[Li-Min], Ma, D.Y.[Dong-Yang], Pan, X.[Xiao], Zhou, Y.F.[Yuan-Feng],
Weak-Boundary Sensitive Superpixel Segmentation Based on Local Adaptive Distance,
CirSysVideo(33), No. 5, May 2023, pp. 2302-2316.
IEEE DOI 2305
Image segmentation, Clustering algorithms, Feature extraction, Standards, Task analysis, Partitioning algorithms, morphology dilation BibRef

Giraud, R.[Rémi], Borba-Pinheiro, R.[Rodrigo], Berthoumieu, Y.[Yannick],
Generalization of the shortest path approach for superpixel segmentation of omnidirectional images,
PR(142), 2023, pp. 109673.
Elsevier DOI 2307
3D Spherical images, Superpixels, Unsupervised segmentation, Shape regularity BibRef

Zeyu, X.[Xie], Xiao, L.[Luo], Defang, Z.[Zhao], Xinyu, C.[Chen],
Fuzzy C-means clustering algorithm based on superpixel merging and multi-feature adaptive fusion measurement,
IET-IPR(18), No. 1, 2024, pp. 140-153.
DOI Link 2401
image segmentation, fuzzy set theory BibRef

Li, H.[Hua], Liang, J.[Junyan], Wu, R.Q.[Rui-Qi], Cong, R.[Runmin], Wu, W.H.[Wen-Hui], Kwong, S.T.W.[Sam Tak Wu],
Stereo Superpixel Segmentation via Decoupled Dynamic Spatial-Embedding Fusion Network,
MultMed(26), 2024, pp. 367-378.
IEEE DOI 2402
Image segmentation, Feature extraction, Task analysis, Image color analysis, Computer science, Collaboration, spatiality embedding BibRef

Huang, S.L.[Shi-Luo], Liu, Z.[Zheng], Jin, W.[Wei], Mu, Y.[Ying],
Superpixel-based multi-scale multi-instance learning for hyperspectral image classification,
PR(149), 2024, pp. 110257.
Elsevier DOI 2403
Multi-instance learning (MIL), Hyperspectral image (HSI) classification, Superpixel BibRef

Chu, B.[Boce], Zhang, M.X.[Meng-Xuan], Ma, K.[Kun], Liu, L.[Long], Wan, J.W.[Jun-Wei], Chen, J.[Jinyong], Chen, J.[Jie], Zeng, H.C.[Hong-Cheng],
Multiobjective Evolutionary Superpixel Segmentation for PolSAR Image Classification,
RS(16), No. 5, 2024, pp. 854.
DOI Link 2403
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Xu, S.[Sen], Wei, S.[Shikui], Ruan, T.[Tao], Zhao, Y.[Yao],
Training Superpixel Network Only Once,
SPLetters(31), 2024, pp. 1284-1288.
IEEE DOI 2405
Training, Semantics, Image color analysis, Classification algorithms, Image reconstruction, Seals, superpixel BibRef

Barcelos, I.B.[Isabela Borlido], de Castro-Belem, F.[Felipe], de Melo-Joao, L.[Leonardo], do Patrocinio, Z.K.G.[Zenilton K. G.], Falcao, A.X.[Alexandre Xavier], Ferzoli-Guimaraes, S.J.[Silvio Jamil],
A Comprehensive Review and New Taxonomy on Superpixel Segmentation,
Surveys(56), No. 8, April 2024, pp. 200.
DOI Link 2405
Survey, Segmentation. Survey, Superpixel Segmentation. Superpixel, image segmentation, survey, image processing BibRef

Xu, S.[Sen], Wei, S.[Shikui], Ruan, T.[Tao], Zhao, Y.[Yao],
ESNet: An Efficient Framework for Superpixel Segmentation,
CirSysVideo(34), No. 7, July 2024, pp. 5389-5399.
IEEE DOI 2407
Feature extraction, Generators, Image segmentation, Computer architecture, Clustering algorithms, Task analysis, deep clustering BibRef

Zhao, T.[Teng], Du, X.P.[Xiao-Ping], Xu, C.[Chen], Jian, H.D.[Hong-Deng], Pei, Z.P.[Zhi-Peng], Zhu, J.J.[Jun-Jie], Yan, Z.Z.[Zhen-Zhen], Fan, X.T.[Xiang-Tao],
SPT-UNet: A Superpixel-Level Feature Fusion Network for Water Extraction from SAR Imagery,
RS(16), No. 14, 2024, pp. 2636.
DOI Link 2408
BibRef

Dang, Y.Y.[Yuan-Yuan], Wei, J.[Junheng], Zhang, L.J.[Li-Jie], Zhong, Z.[Zhaohao],
LDFUNet: Large-Kernel Convolution and Dynamic Fusion Network for Superpixel Segmentation,
CVIDL23(144-147)
IEEE DOI 2403
Deep learning, Image segmentation, Costs, Convolution, Fuses, Heuristic algorithms, Feature extraction, dynamic fusion BibRef

Cosma, R.A.[Radu A.], Knobel, L.[Lukas], van der Linden, P.[Putri], Knigge, D.M.[David M.], Bekkers, E.J.[Erik J.],
Geometric Superpixel Representations for Efficient Image Classification with Graph Neural Networks,
VIPriors23(109-118)
IEEE DOI 2401
BibRef

Eliasof, M.[Moshe], Ben Zikri, N.[Nir], Treister, E.[Eran],
Rethinking Unsupervised Neural Superpixel Segmentation,
ICIP22(3500-3504)
IEEE DOI 2211
Image segmentation, Convolution, Image edge detection, Linear programming, Convolutional neural networks, Proposals, Deep Learning BibRef

Wang, Y.X.[Ya-Xiong], Wei, Y.C.[Yun-Chao], Qian, X.M.[Xue-Ming], Zhu, L.[Li], Yang, Y.[Yi],
AINet: Association Implantation for Superpixel Segmentation,
ICCV21(7058-7067)
IEEE DOI 2203
Convolutional codes, Image segmentation, Convolution, Implants, Jitter, Benchmark testing, Segmentation, grouping and shape, Vision applications and systems BibRef

Wang, X.H.[Xue-Hui], Zhao, Q.Y.[Qing-Yun], Fan, L.[Lei], Zhao, Y.[Yuzhi], Wang, T.T.[Tian-Tian], Yan, Q.[Qiong], Chen, L.[Long],
Semasuperpixel: A Multi-Channel Probability-Driven Superpixel Segmentation Method,
ICIP21(1859-1863)
IEEE DOI 2201
Image segmentation, Statistical analysis, Image color analysis, Heuristic algorithms, Semantics, Measurement uncertainty, Image processing BibRef

Yu, Y.[Yue], Yang, Y.[Yang], Liu, K.Z.[Ke-Zhao],
Edge-Aware Superpixel Segmentation with Unsupervised Convolutional Neural Networks,
ICIP21(1504-1508)
IEEE DOI 2201
Image segmentation, Image edge detection, Predictive models, Prediction algorithms, Convolutional neural networks, Unsupervised Convolutional Neural Networks BibRef

Cai, L.[Lile], Xu, X.[Xun], Liew, J.H.[Jun Hao], Foo, C.S.[Chuan Sheng],
Revisiting Superpixels for Active Learning in Semantic Segmentation with Realistic Annotation Costs,
CVPR21(10983-10992)
IEEE DOI 2111
Deep learning, Costs, Annotations, Atmospheric measurements, Semantics, Benchmark testing BibRef

Zhu, L.[Lei], She, Q.[Qi], Zhang, B.[Bin], Lu, Y.[Yanye], Lu, Z.L.[Zhi-Lin], Li, D.[Duo], Hu, J.[Jie],
Learning the Superpixel in a Non-iterative and Lifelong Manner,
CVPR21(1225-1234)
IEEE DOI 2111
Image segmentation, Convolution, Neural networks, Manuals, Benchmark testing, Pattern recognition, Finite element analysis BibRef

Hartley, T.[Thomas], Sidorov, K.[Kirill], Willis, C.[Christopher], Marshall, D.[David],
SWAG-V: Explanations for Video using Superpixels Weighted by Average Gradients,
WACV22(1576-1585)
IEEE DOI 2202
BibRef
Earlier:
SWAG: Superpixels Weighted by Average Gradients for Explanations of CNNs,
WACV21(423-432)
IEEE DOI 2106
Measurement, Surveillance, Medical services, Computer architecture, Network architecture, Videos, Privacy and Ethics in Vision Action and Behavior Recognition. Measurement, Knowledge engineering, Image analysis, Surveillance, Autonomous vehicles BibRef

Li, X.P.[Xiao-Peng], Xiong, J.L.[Jun-Lin],
Content-Sensitive Superpixels Based on Adaptive Regrowth,
ICPR21(1737-1743)
IEEE DOI 2105
Sensitivity, Shape, Image edge detection, Semantics, Benchmark testing, Pattern recognition, Standards BibRef

Giraud, R.[Rémi], Pinheiro, R.B.[Rodrigo Borba], Berthoumieu, Y.[Yannick],
Generalized Shortest Path-based Superpixels for Accurate Segmentation of Spherical Images,
ICPR21(2650-2656)
IEEE DOI 2105
Measurement, Image segmentation, Shape, Redundancy, Pipelines, Regularity BibRef

Lin, Q.H.[Qing-Hong], Zhong, W.C.[Wei-Chan], Lu, J.L.[Jiang-Lin],
Deep Superpixel Cut for Unsupervised Image Segmentation,
ICPR21(8870-8876)
IEEE DOI 2105
Deep learning, Backpropagation, Image segmentation, Annotations, Clustering methods, Clustering algorithms, Partitioning algorithms BibRef

Azevedo, M.J.C.E.[Marcos J. C. E.], Mello, C.A.B.[Carlos A. B.],
Improvements on the Superpixel Hierarchy Algorithm with Applications to Image Segmentation and Saliency Detection,
ISVC20(I:182-193).
Springer DOI 2103
BibRef

Huang, J.Y.[Jin-Yu], Ding, J.J.[Jian-Jiun],
Generic Image Segmentation in Fully Convolutional Networks by Superpixel Merging Map,
ACCV20(I:723-737).
Springer DOI 2103
BibRef

Li, M., Zou, H., Ma, Q., Sun, J., Cao, X., Qin, X.,
Superpixel Segmentation for Polsar Images Based on Hexagon Initialization and Edge Refinement,
ISPRS20(B2:1225-1232).
DOI Link 2012
BibRef

Zhang, H., Wu, C., Zhang, L., Zheng, H.,
A Novel Centroid Update Approach For Clustering-Based Superpixel Methods And Superpixel-Based Edge Detection,
ICIP20(693-697)
IEEE DOI 2011
Image edge detection, Image color analysis, Noise measurement, Colored noise, Silicon, Gaussian noise, Image segmentation, superpixel segmentation BibRef

An, J.Q.[Jian-Qiao], Shi, Y.C.[Yu-Cheng], Han, Y.H.[Ya-Hong], Sun, M.J.[Mei-Jun], Tian, Q.[Qi],
Extract and Merge: Superpixel Segmentation with Regional Attributes,
ECCV20(XXX: 155-170).
Springer DOI 2010
BibRef

Yang, F., Sun, Q., Jin, H., Zhou, Z.,
Superpixel Segmentation With Fully Convolutional Networks,
CVPR20(13961-13970)
IEEE DOI 2008
Convolution, Task analysis, Image segmentation, Benchmark testing, Feature extraction, Neural networks, Computer architecture BibRef

Ye, Z., Yi, R., Yu, M., Liu, Y., He, Y.,
Fast Computation of Content-Sensitive Superpixels and Supervoxels Using Q-Distances,
ICCV19(3769-3778)
IEEE DOI 2004
computational complexity, computational geometry, graph theory, image segmentation, video signal processing, video applications BibRef

Khan, N., Zhang, Q., Kasser, L., Stone, H., Kim, M.H., Tompkin, J.,
View-Consistent 4D Light Field Superpixel Segmentation,
ICCV19(7810-7818)
IEEE DOI 2004
image colour analysis, image segmentation, pattern clustering, EPI spaces, occlusion-aware clustering, Robustness BibRef

Uziel, R., Ronen, M., Freifeld, O.,
Bayesian Adaptive Superpixel Segmentation,
ICCV19(8469-8478)
IEEE DOI 2004
Code, Segmentation.
WWW Link. Bayes methods, image colour analysis, image representation, image segmentation, mixture models, nonparametric statistics, Image color analysis BibRef

Ye, L., Zhu, L., Kang, X., Ming, A.,
Adaptive Occlusion Boundary Extraction for Depth Inference,
ICIP19(4025-4029)
IEEE DOI 1910
occlusion boundary extraction, superpixel segmentation, cost-sensitive boosting classification, depth inference BibRef

Wu, C., Zhang, L., Zhang, H., Yan, H.,
Improved Superpixel-Based Fast Fuzzy C-Means Clustering for Image Segmentation,
ICIP19(1455-1459)
IEEE DOI 1910
Superpixel, color image segmentation, Fuzzy SLIC, SFFCM BibRef

Giraud, R., Ta, V., Papadakis, N., Berthoumieu, Y.,
Texture-Aware Superpixel Segmentation,
ICIP19(1465-1469)
IEEE DOI 1910
Superpixels, Texture, Patch, Segmentation BibRef

Chuchvara, A., Gotchev, A.,
Content-Adaptive Superpixel Segmentation Via Image Transformation,
ICIP19(1505-1509)
IEEE DOI 1910
Superpixel, image segmentation BibRef

de Almeida, C.S.J.[Carolina Stephanie Jerônimo], Cousty, J.[Jean], Perret, B.[Benjamin], Patrocínio, Jr., Z.K.G.[Zenilton Kleber G.], Guimarães, S.J.F.[Silvio Jamil F.],
Label Propagation Guided by Hierarchy of Partitions for Superpixel Computation,
CIAP19(II:3-13).
Springer DOI 1909
BibRef

de Gregorio, D.[Daniele], Palli, G.[Gianluca], di Stefano, L.[Luigi],
Let's Take a Walk on Superpixels Graphs: Deformable Linear Objects Segmentation and Model Estimation,
ACCV18(II:662-677).
Springer DOI 1906
BibRef

Derksen, D., Inglada, J., Michel, J.,
Scaling Up SLIC Superpixels Using a Tile-Based Approach,
GeoRS(57), No. 5, May 2019, pp. 3073-3085.
IEEE DOI 1905
geophysical image processing, geophysical techniques, image segmentation, remote sensing, tile-based approach, superpixel segmentation BibRef

Tu, W., Liu, M., Jampani, V., Sun, D., Chien, S., Yang, M., Kautz, J.,
Learning Superpixels with Segmentation-Aware Affinity Loss,
CVPR18(568-576)
IEEE DOI 1812
Image segmentation, Image edge detection, Feature extraction, Erbium, Clustering algorithms, Task analysis, Seals BibRef

Davydow, A., Nikolenko, S.,
Land Cover Classification with Superpixels and Jaccard Index Post-Optimization,
DeepGlobe18(280-2804)
IEEE DOI 1812
Indexes, Training, Satellites, Task analysis, Image segmentation, Standards BibRef

Wei, Y., Chang, M., Ying, Y., Lim, S.N., Lyu, S.,
Explain Black-box Image Classifications Using Superpixel-based Interpretation,
ICPR18(1640-1645)
IEEE DOI 1812
Visualization, Image color analysis, Handheld computers, Histograms, Probabilistic logic, Birds, Neural networks BibRef

Jampani, V.[Varun], Sun, D.[Deqing], Liu, M.Y.[Ming-Yu], Yang, M.H.[Ming-Hsuan], Kautz, J.[Jan],
Superpixel Sampling Networks,
ECCV18(VII: 363-380).
Springer DOI 1810
BibRef

Leblond, A.[Antoine], Kauffmann, C.[Claude],
RAIC: Robust Adaptive Image Clustering,
ICIP18(3678-3682)
IEEE DOI 1809
Image segmentation, Clustering algorithms, Robustness, Image edge detection, Image reconstruction, Object segmentation, BibRef

Maierhofer, G., Heydecker, D., Aviles-Rivero, A.I., Alsaleli, S.M., Schonlieb, C.B.,
Peekaboo-Where are the Objects? Structure Adjusting Superpixels,
ICIP18(3693-3697)
IEEE DOI 1809
Image segmentation, Clustering algorithms, Heuristic algorithms, Image color analysis, Measurement, Visualization, Image texture analysis BibRef

Suzuki, T., Akizuki, S., Kato, N., Aoki, Y.,
Superpixel Convolution for Segmentation,
ICIP18(3249-3253)
IEEE DOI 1809
Convolution, Kernel, Convolutional neural networks, Task analysis, Image segmentation, Spatial resolution, Saliency Object Detection BibRef

Zhang, L., Wang, Y., Sun, Y.,
Salient Target Detection Based on the Combination of Super-Pixel and Statistical Saliency Feature Analysis for Remote Sensing Images,
ICIP18(2336-2340)
IEEE DOI 1809
Feature extraction, Image segmentation, Remote sensing, Histograms, Image color analysis, Interference, Analytical models, thresholding BibRef

Luengo, I.[Imanol], Basham, M.[Mark], French, A.[Andrew],
SMURFS: Superpixels from Multi-scale Refinement of Super-regions,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Zhang, N., Zhang, L.,
SSGD: Superpixels using the Shortest Gradient Distance,
ICIP17(3869-3873)
IEEE DOI 1803
Clustering algorithms, Euclidean distance, Image color analysis, Image edge detection, Image segmentation, shortest gradient distance BibRef

Wilms, C.[Christian], Frintrop, S.[Simone],
Edge Adaptive Seeding for Superpixel Segmentation,
GCPR17(333-344).
Springer DOI 1711
BibRef

Yeo, D., Son, J., Han, B., Han, J.H.,
Superpixel-Based Tracking-by-Segmentation Using Markov Chains,
CVPR17(511-520)
IEEE DOI 1711
Absorption, Image segmentation, Markov processes, Support vector machines, Target tracking, Transient, analysis BibRef

Zhu, H., Zhang, Q., Wang, Q.,
4D Light Field Superpixel and Segmentation,
CVPR17(6709-6717)
IEEE DOI 1711
Image color analysis, Image segmentation, Imaging, Lenses, Measurement, BibRef

Lo, C.K., Chang, L.W.,
Unsupervised image segmentation using defocus map and superpixel grouping,
MVA17(141-144)
DOI Link 1708
Computer science, Estimation, Image color analysis, Image edge detection, Image segmentation, Organizations, Silicon BibRef

Wang, X., Zhou, Y.[Yun], Ning, C.,
An improved superpixel-based saliency detection method,
ICIVC17(710-714)
IEEE DOI 1708
Bayes methods, Iterative methods, Sun, center-surrounding, normalized cut, saliency detection, sparse representation, superpixel BibRef

Mikesell, D.[Derek], Hicks, I.V.[Illya V.],
Image Segmentation via Weighted Carving Decompositions,
IWCIA17(268-279).
Springer DOI 1706
BibRef

Huang, C.R.[Chun-Rong], Wang, W.A., Lin, S.Y.[Szu-Yu], Lin, Y.Y.[Yen-Yu],
USEQ: Ultra-fast superpixel extraction via quantization,
ICPR16(1965-1970)
IEEE DOI 1705
Computational efficiency, Estimation, Image color analysis, Image segmentation, Iterative methods, Optimization, Quantization (signal), image segmentation, superpixel BibRef

Rubio, A., Yu, L.L.[Long-Long], Simo-Serra, E., Moreno-Noguer, F.,
BASS: Boundary-Aware Superpixel Segmentation,
ICPR16(2824-2829)
IEEE DOI 1705
Clustering algorithms, Image color analysis, Image edge detection, Image segmentation, Measurement, Standards BibRef

Agoes, A.S.[Ali Suryaperdana], Hu, Z.C.[Zhen-Cheng], Matsunaga, N.[Nobutomo],
DSLIC: A Superpixel Based Segmentation Algorithm for Depth Image,
3DModelApp16(II: 77-87).
Springer DOI 1704
BibRef

Liu, Y.[Yuan], Lai, S.Q.[Shang-Qi], Du, T.Y.[Tian-Yi], Yu, Y.Z.[Yi-Zhou],
Hybrid superpixel segmentation,
ICVNZ15(1-6)
IEEE DOI 1701
greedy algorithms BibRef

Gu, X., Jeremiah, D., Martin, K.,
A hierarchical segmentation tree for superpixel-based image segmentation,
ICVNZ16(1-6)
IEEE DOI 1701
Bipartite graph BibRef

Kurlin, V.[Vitaliy], Harvey, D.[Donald],
Superpixels Optimized by Color and Shape,
EMMCVPR17(297-311).
Springer DOI 1805
BibRef

Forsythe, J.[Jeremy], Kurlin, V.[Vitaliy], Fitzgibbon, A.W.[Andrew W.],
Resolution-Independent Superpixels Based on Convex Constrained Meshes Without Small Angles,
ISVC16(I: 223-233).
Springer DOI 1701
BibRef

Li, R.[Rui], Fang, L.[Lu],
Cluster Sensing Superpixel and Grouping,
Microscopy16(1350-1358)
IEEE DOI 1612
BibRef

Sheikh, R.[Rasha], Garbade, M.[Martin], Gall, J.[Juergen],
Real-Time Semantic Segmentation with Label Propagation,
CVRoads16(II: 3-14).
Springer DOI 1611
BibRef

Ahmed, Q.A.[Qazi Aitezaz], Akhtar, M.[Mahmood],
Runtime Performance Enhancement of a Superpixel Based Saliency Detection Model,
ICIAR16(120-130).
Springer DOI 1608
BibRef

Ates, H.F.[Hasan F.], Sunetci, S.[Sercan],
Improving Semantic Segmentation with Generalized Models of Local Context,
CAIP17(II: 320-330).
Springer DOI 1708
BibRef

Ates, H.F.[Hasan F.], Sunetci, S.[Sercan], Ak, K.E.[Kenan E.],
Kernel Likelihood Estimation for Superpixel Image Parsing,
ICIAR16(234-242).
Springer DOI 1608
BibRef

Siva, P.[Parthipan], Scharfenberger, C.[Christian], Ben Daya, I.[Ibrahim], Mishra, A.[Akshaya], Wong, A.[Alexander],
Return of grid seams: A superpixel algorithm using discontinuous multi-functional energy seam carving,
ICIP15(1334-1338)
IEEE DOI 1512
Discontinuous; Multi-functional; Seam Carving; Superpixels BibRef

Freifeld, O.[Oren], Li, Y.X.[Yi-Xin], Fisher, J.W.[John W.],
A fast method for inferring high-quality simply-connected superpixels,
ICIP15(2184-2188)
IEEE DOI 1512
Superpixel BibRef

Xu, X.[Xin], Mu, N.[Nan], Zhang, H.[Hong], Fu, X.W.[Xiao-Wei],
Salient object detection from distinctive features in low contrast images,
ICIP15(3126-3130)
IEEE DOI 1512
low contrast image; saliency map; salient object detection; superpixel BibRef

Jia, S.Y.[Shao-Yong], Geng, S.J.[Shi-Jie], Gu, Y.[Yun], Yang, J.[Jie], Shi, P.F.[Peng-Fei], Qiao, Y.[Yu],
NSLIC: SLIC superpixels based on nonstationarity measure,
ICIP15(4738-4742)
IEEE DOI 1512
nSLIC; nonstationarity measure; super-pixel BibRef

Ince, K.G.[Kutalmis Gokalp], Cigla, C.[Cevahir], Alatan, A.A.[A. Aydin],
LASP: Local adaptive super-pixels,
ICIP15(4092-4096)
IEEE DOI 1512
Over segmentation; clustering; super pixel BibRef

Verdoja, F.[Francesco], Grangetto, M.[Marco],
Fast Superpixel-Based Hierarchical Approach to Image Segmentation,
CIAP15(I:364-374).
Springer DOI 1511
BibRef

Picciau, G.[Giulia], Simari, P.[Patricio], Iuricich, F.[Federico], de Floriani, L.[Leila],
Supertetras: A Superpixel Analog for Tetrahedral Mesh Oversegmentation,
CIAP15(I:375-386).
Springer DOI 1511
BibRef

Stutz, D.[David],
Superpixel Segmentation: An Evaluation,
GCPR15(555-562).
Springer DOI 1511
BibRef

Sullivan, K.[Keith], Lawson, W.[Wallace], Sofge, D.[Donald],
Improving superpixel boundaries using information beyond the visual spectrum,
PBVS15(105-112)
IEEE DOI 1510
Clustering algorithms BibRef

Li, Z.Q.[Zheng-Qin], Chen, J.S.[Jian-Sheng],
Superpixel segmentation using Linear Spectral Clustering,
CVPR15(1356-1363)
IEEE DOI 1510
BibRef

Giordano, D.[Daniela], Murabito, F.[Francesca], Palazzo, S.[Simone], Spampinato, C.[Concetto],
Superpixel-based video object segmentation using perceptual organization and location prior,
CVPR15(4814-4822)
IEEE DOI 1510
BibRef

Yan, J.J.[Jun-Jie], Yu, Y.N.[Yi-Nan], Zhu, X.Y.[Xiang-Yu], Lei, Z.[Zhen], Li, S.Z.[Stan Z.],
Object detection by labeling superpixels,
CVPR15(5107-5116)
IEEE DOI 1510
BibRef

Fu, P.[Peng], Li, C.Y.[Chang-Yang], Sun, Q.S.[Quan-Sen], Cai, W.D.[Wei-Dong], Feng, D.D.[David Dagan],
Image noise level estimation based on a new adaptive superpixel classification,
ICIP14(2649-2653)
IEEE DOI 1502
Clustering algorithms BibRef

Pei, S.C.[Soo-Chang], Chang, W.W.[Wen-Wen], Shen, C.T.[Chih-Tsung],
Saliency detection using superpixel belief propagation,
ICIP14(1135-1139)
IEEE DOI 1502
Adaptation models BibRef

Jia, S.X.[Shi-Xiang], Zhang, C.M.[Cai-Ming],
Fast and robust image segmentation using an superpixel based FCM algorithm,
ICIP14(947-951)
IEEE DOI 1502
Classification algorithms BibRef

Gu, X.B.[Xian-Bin], Deng, J.D.[Jeremiah D.], Purvis, M.K.[Martin K.],
Improving superpixel-based image segmentation by incorporating color covariance matrix manifolds,
ICIP14(4403-4406)
IEEE DOI 1502
Bipartite graph BibRef

Pan, X.[Xiao], Zhou, Y.F.[Yuan-Feng], Zhang, C.M.[Cai-Ming], Liu, Q.[Qian],
Flooding based superpixels generation with color, compactness and smoothness constraints,
ICIP14(4432-4436)
IEEE DOI 1502
Clustering algorithms BibRef

Neubert, P.[Peer], Protzel, P.[Peter],
Compact Watershed and Preemptive SLIC: On Improving Trade-offs of Superpixel Segmentation Algorithms,
ICPR14(996-1001)
IEEE DOI 1412
Adaptive optics BibRef

Massoudifar, P.[Pegah], Rangarajan, A.[Anand], Gader, P.[Paul],
Superpixel Estimation for Hyperspectral Imagery,
PBVS14(287-292)
IEEE DOI 1409
BibRef

Gould, S.[Stephen], Zhao, J.C.[Jie-Cheng], He, X.M.[Xu-Ming], Zhang, Y.H.[Yu-Hang],
Superpixel Graph Label Transfer with Learned Distance Metric,
ECCV14(I: 632-647).
Springer DOI 1408
fast approximate nearest neighbor algorithm for semantic segmentation BibRef

Yang, M.Y.,
Image Segmentation by Bilayer Superpixel Grouping,
ACPR13(552-556)
IEEE DOI 1408
computer vision BibRef

Siva, P.[Parthipan], Wong, A.[Alexander],
Grid Seams: A Fast Superpixel Algorithm for Real-Time Applications,
CRV14(127-134)
IEEE DOI 1406
Accuracy BibRef

Zhu, G.[Gao], Ming, Y.S.[Yan-Sheng], Li, H.D.[Hong-Dong],
Object Cut as Minimum Ratio Cycle in a Superpixel Boundary Graph,
DICTA13(1-6)
IEEE DOI 1402
graph theory BibRef

Pham, T.Q.,
Parallel Implementation of Geodesic Distance Transform with Application in Superpixel Segmentation,
DICTA13(1-8)
IEEE DOI 1402
application program interfaces BibRef

Wang, X.F.[Xiao-Fang], Li, H.B.[Hui-Bin], Bichot, C.E.[Charles-Edmond], Masnou, S.[Simon], Chen, L.M.[Li-Ming],
A graph-cut approach to image segmentation using an affinity graph based on L0-sparse representation of features,
ICIP13(4019-4023)
IEEE DOI 1402
l0 affinity graph BibRef

Wang, X.F.[Xiao-Fang], Zhu, C.[Chao], Bichot, C.E.[Charles-Edmond], Masnou, S.[Simon],
Graph-based image segmentation using weighted color patch,
ICIP13(4064-4068)
IEEE DOI 1402
Image segmentation; affinity graph; normalized cuts; weighted color patch BibRef

Wang, X.F.[Xiao-Fang], Li, H.B.[Hui-Bin], Masnou, S.[Simon],
Sparse Coding and Mid-Level Superpixel-Feature for l0-Graph Based Unsupervised Image Segmentation,
CAIP13(II:160-168).
Springer DOI 1311
BibRef

Li, L.[Liang], Feng, W.[Wei], Wan, L.[Liang], Zhang, J.W.[Jia-Wan],
Maximum Cohesive Grid of Superpixels for Fast Object Localization,
CVPR13(3174-3181)
IEEE DOI 1309
Maximum grid of superpixels; dynamic programming; object localization BibRef

Shu, G.[Guang], Dehghan, A.[Afshin], Shah, M.[Mubarak],
Improving an Object Detector and Extracting Regions Using Superpixels,
CVPR13(3721-3727)
IEEE DOI 1309
BibRef

Liu, H.[Han], Qu, Y.Y.[Yan-Yun], Wu, Y.[Yang], Wang, H.Z.[Han-Zi],
Class-Specified Segmentation with Multi-scale Superpixels,
CompPhot12(I:158-169).
Springer DOI 1304
BibRef

Li, Z.G.[Zhen-Guo], Wu, X.M.[Xiao-Ming], Chang, S.F.[Shih-Fu],
Segmentation using superpixels: A bipartite graph partitioning approach,
CVPR12(789-796).
IEEE DOI 1208
BibRef

Zhang, Y.H.[Yu-Hang], Hartley, R.I.[Richard I.], Mashford, J.[John], Burn, S.[Stewart],
Superpixels, Occlusion and Stereo,
DICTA11(84-91).
IEEE DOI 1205
BibRef
And:
Superpixels via pseudo-Boolean optimization,
ICCV11(1387-1394).
IEEE DOI 1201
BibRef

Lowe, R.J.[Richard J.], Nixon, M.S.[Mark S.],
Evolving Content-Driven Superpixels for Accurate Image Representation,
ISVC11(I: 192-201).
Springer DOI 1109
BibRef

Engel, D.[David], Spinello, L.[Luciano], Triebel, R.[Rudolph], Siegwart, R.[Roland], Bülthoff, H.H.[Heinrich H.], Curio, C.[Cristóbal],
Medial Features for Superpixel Segmentation,
MVA09(248-).
PDF File. 0905
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Veksler, O.[Olga], Boykov, Y.Y.[Yuri Y.], Mehrani, P.[Paria],
Superpixels and Supervoxels in an Energy Optimization Framework,
ECCV10(V: 211-224).
Springer DOI 1009
BibRef

Yuan, Y.[Yuan], Ma, L.H.[Li-Hong], Lu, H.Q.[Han-Qing],
Image Segmentation Based on Supernodes and Region Size Estimation,
ACIVS08(xx-yy).
Springer DOI 0810
BibRef

Warrell, J.[Jonathan], Moore, A.P.[Alastair P.], Prince, S.J.D.[Simon J.D.],
Vistas: Hierarchial boundary priors using multiscale conditional random fields,
BMVC09(xx-yy).
PDF File. 0909
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Moore, A.P.[Alastair P.], Prince, S.J.D.[Simon J.D.], Warrell, J.[Jonathan], Mohammed, U.[Umar], Jones, G.[Graham],
Scene shape priors for superpixel segmentation,
ICCV09(771-778).
IEEE DOI 0909
BibRef
Earlier:
Superpixel lattices,
CVPR08(1-8).
IEEE DOI 0806
Oversegmentation. BibRef

Hanbury, A.[Allan],
How Do Superpixels Affect Image Segmentation?,
CIARP08(178-186).
Springer DOI 0809
BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Watershed Algorithms, Watershed Segmentation .