8.6 Techniques for Model Guided Segmentation, Context in Segmentation

Shaheen, S.I.[Samir I.], Levine, M.D.[Martin D.],
Some Experiments with the Interpretation Strategy of a Modular Computer Vision System,
PR(14), No. 1-6, 1981, pp. 87-90.
Elsevier DOI 0309
BibRef

Levine, M.D., and Shaheen, S.I.,
A Modular Computer Vision System for Picture Segmentation and Interpretation,
PAMI(3), No. 5, September 1981, pp. 540-556. BibRef 8109
Earlier:
A Modular Computer Vision System for Picture Segmentation,
PRIP79(523-539). BibRef

Kropatsch, W.G.,
Segmentation of Digital Images Using a Priori Information about the Expected Image Contents,
PDA83(107-132). BibRef 8300

Zamperoni, P.[Piero],
Model-Based Segmentation of Grey-Tone Images,
IVC(2), No. 3, August 1984, pp. 123-133.
Elsevier DOI BibRef 8408

Zamperoni, P.[Piero],
Feature Extraction by Rank-Order Filtering for Image Segmentation,
PRAI(2), 1988, pp. 301-319. BibRef 8800

Zamperoni, P.[Piero],
Some Adaptive Rank Order Filters for Image Enhancement,
PRL(11), 1990, pp. 81-86. BibRef 9000

Zamperoni, P.[Piero],
Mode Estimation and Non-Linear Image Smoothing with Adaptive Rank-Order Filters,
Draft1989. BibRef 8900

Hyde, J., Fullwood, J.A., Corrall, D.R.,
An Approach to Knowledge-Driven Segmentation,
IVC(3), No. 4, November 1985, pp. 198-205.
Elsevier DOI BibRef 8511

Matsuyama, T.[Takashi],
Expert Systems for Image Processing: Knowledge-Based Composition of Image Analysis Processes,
CVGIP(48), No. 1, October 1989, pp. 22-49.
Elsevier DOI BibRef 8910
Earlier: ICPR88(I: 125-133).
IEEE DOI 8811
Rule Based Systems. System: SIGMA. This builds on the general systems such as SIGMA and is directed toward segmentation. BibRef

Nagao, M.[Makoto], Matsuyama, T.[Takashi], Ikeda, Y.[Yoshio],
Region Extraction and Shape Analysis in Aerial Photographs,
CGIP(10), No. 3, July 1979, pp. 195-223.
Elsevier DOI BibRef 7907
Earlier:
Region Extraction and Shape Analysis of Aerial Photographs,
ICPR78(620-628). This uses a global to detailed analysis technique. BibRef

Tenenbaum, J.M., and Barrow, H.G.,
Experiments in Interpretation Guided Segmentation,
AI(8), No. 3, June 1977, pp. 241-274.
Elsevier DOI BibRef 7706
And: SRI AICenter-TN 123, March 1976. BibRef
And:
IGS: A Paradigm for Integrating Image Segmentation and Interpretation,
PRAI-76(472-507). BibRef
And: ICPR76(504-513). BibRef
And: CMetImAly77(435-444). Segmentation, Knowledge. System: IGS. The key idea is that image elements can be reliably clustered into regions if semantic interpretations are used in addition to the raw image values. This builds on the interpretation ideas of MSYS (
See also MSYS: A System for Reasoning about Scenes. ). Unlike the work in Yakimovsky and Feldman, the relations between different types of regions are either possible or impossible. Initial interpretations are based on the image data, but extra interpretations at this point are not harmful. An iterative procedure is used to eliminate interpretations that are not valid given all the possible interpretations of the neighbors. When adjacent regions have the same interpretation they can be merged. This method requires a very specific model of the possible scene to provide any benefit. BibRef

Chassery, J.M., and Garbay, C.,
An Iterative Segmentation Method Based on a Contextual Color and Shape Criterion,
PAMI(6), No. 6, November 1984, pp. 794-799. BibRef 8411
Earlier: ICPR84(642-644). Segmentation, Color. BibRef

Garbay, C.,
Image Structure Representation and Processing: A Discussion of Some Segmentation Methods in Cytology,
PAMI(8), No. 2, March 1986, pp. 140-146. BibRef 8603
Earlier:
Knowledge and Strategies for Image Segmentation,
ICPR86(669-671). BibRef

Vasselle, B., Giraudon, G.,
A Multiscale Regularity Measure as a Geometric Criterion for Image Segmentation,
MVA(7), No. 4, 1994, pp. 229-236. BibRef 9400

Houzelle, S., and Giraudon, G.,
Model Based Region Segmentation Using Cooccurrence Matrices,
CVPR92(636-639).
IEEE DOI Base segmentation on second order statistics. BibRef 9200

Tucker, L.W., (Cornell and Polytechnic I of NY)
Model-Guided Segmentation Using Quadtrees,
ICPR84(216-219). BibRef 8400
And:
Control Strategy for an Expert Vision System Using Quadtree Refinement,
CVWS84(214-218). Innovative use of quadtrees in segmentation. BibRef

Kestner, W.,
Segmentation and Abstract Interpretation in an Image Understanding System,
ICPR82(1011-1013). BibRef 8200
Earlier:
Considerations About Knowledge-Based Image Interpretation,
ICPR80(330-332). High level discussion of a high level system for image analysis using different descriptions and the different levels of abstraction and different programs to link between the different levels. BibRef

Kestner, W., Bohner, M., Scharf, R., and Sties, M.,
Object Guided Segmentation of Aerial Images,
ICPR80(529-531). (Karlsruhe). Starting from (interactively selected) elements (line segments, rough contours, centers of regions) expand to complete lines or regions. Interactive. Basically grow the object as long as the feature values remain constant or change gradually. Boundaries between regions handled by pixel level analysis - initial expansion is based on features over larger areas. BibRef 8000

Selfridge, P.G., Sloan, Jr., K.R.,
Reasoning About Images: Using Meta-Knowledge in Aerial Image Understanding,
IJCAI81(755-757). BibRef 8100
Earlier:
Reasoning About Images: Application to Aerial Image Understanding,
DARPA81(1-6). BibRef
And:
Locating Objects under Different Conditions: An Example in Aerial Image Understanding,
PRIP81(470-472). (Rochester). Segmentation and analysis system to use partial results to perform matching then update parameters for segmentation. Given an appearance model, an initial threshold based segmentation is applied, e.g., building and shadow. Candidate regions are matched to select most likely, and the threshold is varied to find the region which produces the best match. Extensive use of partial matching results. BibRef

Selfridge, P.G.,
Reasoning About Success and Failure in Aerial Image Understanding,
Ph.D.Thesis (CS), May 1983, BibRef 8305 Univ. of Rochester-TR-103. BibRef
And: with Sloan, Jr., K.R., PRIP82(44-49). Segmentation problems: what to look for, which technique to use, and the parameters for the procedure. Goal directed processing at every stage. Given a model of the objects, pick a segmentation method (thresholding), set arbitrary thresholds (almost), change them to get candidate regions, evaluate based on other features. Then in conjunction with higher level model descriptions composed into groups or structures and look at where other pieces can occur. BibRef

Sloan, Jr., K.R.,
Representation and Communication of Image-Related Information,
DARPAN79(136-139). BibRef 7900

Mota, F.A., and Velasco, F.R.D.,
A Method for the Analysis of Ambiguous Segmentations of Images,
PAMI(8), No. 6, November 1986, pp. 755-760. Seems to be a discussion of using the model (interpretation) information to eliminate segmentation "errors." BibRef 8611

Mason, P., Buggy, T.W.,
Knowledge-Based Segmentation of Sonar Data,
IVC(5), No. 2, May 1987, pp. 127-131.
Elsevier DOI BibRef 8705

Sunil Kumar, K., Desai, U.B.,
Joint segmentation and image interpretation,
PR(32), No. 4, April 1999, pp. 577-589.
Elsevier DOI BibRef 9904
Earlier: ICIP96(I: 853-856).
IEEE DOI 9610

See also Image Interpretation Using Bayesian Networks. BibRef

Kopparapu, S.I.K.[Sun-Il K.], Desai, U.B.[Uday B.],
Bayesian Approach to Image Interpretation,
KluwerBoston, July 2001. ISBN 0-7923-7372-3.
WWW Link. Combine segmentation and interpretation modules. BibRef 0107

Figov, Z., Tal, Y., and Koppel, M.[Moshe],
Detecting and Removing Shadows,
ICCGI04(xx-yy).
PDF File. BibRef 0400

Kamath, N.[Nidish], Kopparapu, S.I.K.[Sun-Il K.], Desai, U.B., Dugud, R.[Rakesh],
Joint Segmentation and Image Interpretation Using Hidden Markov Models,
ICPR98(Vol II: 1840-1842).
IEEE DOI 9808
BibRef

Molander, S., Broman, H.,
Knowledge-based segmentation and state-based control in image analysis: Two examples from the biomedical domain,
SP(32), No. 1-2, 1993, pp. 201-215. BibRef 9300

Ezquerra, N.[Norberto], Mullick, R.[Rakesh],
Knowledge-Guided Segmentation of 3D Imagery,
GMIP(58), No. 6, November 1996, pp. 510-523. 9701
BibRef

Ezquerra, N.[Norberto], Mullick, R.[Rakesh],
An Approach to 3D Pose Estimation,
TOG(15), No. 2, April 1996, pp. 99-120. BibRef 9604

Haker, S., Sapiro, G., Tannenbaum, A.,
Knowledge-Based Segmentation of SAR Data with Learned Priors,
IP(9), No. 2, February 2000, pp. 299-301.
IEEE DOI 0003
BibRef

Baujard, O., Garbay, C.,
KISS: A Multiagent Segmentation System,
OptEng(32), No. 6, June 1993, pp. 1235-1249. BibRef 9306

Manon, G., Pesty, S., Garbay, C.,
KIDS (knowledge-based diagnosis system)-a specialized architecture,
ICPR88(II: 995-997).
IEEE DOI 8811
BibRef

Rushing, J.A., Ranganath, H., Hinke, T.H., Graves, S.J.,
Image segmentation using association rule features,
IP(11), No. 5, May 2002, pp. 558-567.
IEEE DOI 0206
BibRef

Evans, C., Jones, R., Svalbe, I., Berman, M.,
Segmenting Multispectral Landsat TM Images into Field Units,
GeoRS(40), No. 5, May 2002, pp. 1054-1064.
IEEE Top Reference. 0206
BibRef

Goldberger, J.[Jacob], Greenspan, H.[Hayit],
Context-Based Segmentation of Image Sequences,
PAMI(28), No. 3, March 2006, pp. 463-468.
IEEE DOI 0602
New frames segmented based on the segmentation of prior frames. BibRef

Eriksson, A.P.[Anders P.], Olsson, C.[Carl], Kahl, F.[Fredrik],
Normalized Cuts Revisited: A Reformulation for Segmentation with Linear Grouping Constraints,
JMIV(39), No. 1, January 2011, pp. 45-61.
WWW Link. 1101
BibRef
Earlier: ICCV07(1-8).
IEEE DOI 0710
BibRef
And:
Image Segmentation with Context,
SCIA07(283-292).
Springer DOI 0706
BibRef

Toyoda, T.[Takahiro], Hasegawa, O.[Osamu],
Random Field Model for Integration of Local Information and Global Information,
PAMI(30), No. 8, August 2008, pp. 1483-1489.
IEEE DOI 0806
BibRef

Toyoda, T.[Takahiro], Tagami, K.[Keisuke], Hasegawa, O.[Osamu],
Integration of Top-down and Bottom-up Information for Image Labeling,
CVPR06(I: 1106-1113).
IEEE DOI 0606
BibRef

Rahmani, R.[Rouhollah], Goldman, S.A.[Sally A.], Zhang, H.[Hui], Cholleti, S.R.[Sharath R.], Fritts, J.E.[Jason E.],
Localized Content-Based Image Retrieval,
PAMI(30), No. 11, November 2008, pp. 1902-1912.
IEEE DOI 0809
System, ACCIO. Interested only in part of the iamge. Extend traditional segmentation-based and salient point-based techniques to capture content. Salient points using SPARSE (filtered Haar-wavelet points) Wavelet (Variably Split Window with Neighbor) SIFT (
See also Distinctive Image Features from Scale-Invariant Keypoints. ) BibRef

Zhang, H.[Hui], Goldman, S.A.[Sally A.],
Image Segmentation using Salient Points-Based Object Templates,
ICIP06(765-768).
IEEE DOI 0610
BibRef

Gould, S.[Stephen], Rodgers, J.[Jim], Cohen, D.[David], Elidan, G.[Gal], Koller, D.[Daphne],
Multi-Class Segmentation with Relative Location Prior,
IJCV(80), No. 3, December 2008, pp. xx-yy.
Springer DOI 0810
Using spatial relations in segmentation. BibRef

Gould, S.[Stephen], Fulton, R.[Richard], Koller, D.[Daphne],
Decomposing a scene into geometric and semantically consistent regions,
ICCV09(1-8).
IEEE DOI 0909
BibRef

Gould, S.[Stephen],
Multiclass pixel labeling with non-local matching constraints,
CVPR12(2783-2790).
IEEE DOI 1208
BibRef

Crevier, D.[Daniel],
Image segmentation algorithm development using ground truth image data sets,
CVIU(112), No. 2, November 2008, pp. 143-159.
Elsevier DOI 0811
BibRef
Earlier:
Extracting Salient Objects from Operator-Framed Images,
CRV07(36-43).
IEEE DOI 0705
Image segmentation; Performance measures; Ground truth images; Image data sets; Parameter learning; Texture; Edge detection; Region growing; Data engineering tools and techniques; Stochastic optimization BibRef

Borenstein, E.[Eran], Ullman, S.[Shimon],
Combined Top-Down/Bottom-Up Segmentation,
PAMI(30), No. 12, December 2008, pp. 2109-2125.
IEEE DOI 0811
BibRef
Earlier:
Learning to Segment,
ECCV04(Vol III: 315-328).
Springer DOI 0405
BibRef
Earlier:
Class-Specific, Top-Down Segmentation,
ECCV02(II: 109 ff.).
Springer DOI 0205
Guided by a stored representation of the shape. Find the foreground objects, they generally are more fragmented (textured?). BibRef

Borenstein, E.[Eran], Sharon, E.[Eitan], Ullman, S.[Shimon],
Combining Top-Down and Bottom-Up Segmentation,
PercOrg04(46).
IEEE DOI 0502
BibRef

Ross, M.G.[Michael G.], Kaelbling, L.P.[Leslie Pack],
Segmentation According to Natural Examples: Learning Static Segmentation from Motion Segmentation,
PAMI(31), No. 4, April 2009, pp. 661-676.
IEEE DOI 0903
BibRef
Earlier:
Learning object segmentation from video data,
MIT AIM-2003-022, September 8, 2003.
WWW Link. 0501
Trained on video that uses background subtraction to find objects in video. Then segment static scene using learned values. BibRef

Burrus, N.[Nicolas], Bernard, T.M.[Thierry M.], Jolion, J.M.[Jean-Michel],
Image segmentation by a contrario simulation,
PR(42), No. 7, July 2009, pp. 1520-1532.
Elsevier DOI 0903
BibRef
Earlier:
Bottom-Up and Top-Down Object Matching Using Asynchronous Agents and a Contrario Principles,
CVS08(xx-yy).
Springer DOI 0805
Segmentation; A contrario reasoning; Statistical image processing; Monte-Carlo simulation. hierarchy of independent agents. Strength based on relevance of visual data. BibRef

Burrus, N.[Nicolas], Bernard, T.M.[Thierry M.],
Adaptive Vision Leveraging Digital Retinas: Extracting Meaningful Segments,
ACIVS06(220-231).
Springer DOI 0609
Extract segments that depart from the norm. BibRef

Sabuncu, M.R., Yeo, B.T.T., van Leemput, K., Fischl, B., Golland, P.,
A Generative Model for Image Segmentation Based on Label Fusion,
MedImg(29), No. 10, October 2010, pp. 1714-1729.
IEEE DOI 1011
Segmentation given a training set. Pairwise registration between image and label maps. BibRef

Wachinger, C., Fritscher, K., Sharp, G., Golland, P.,
Contour-Driven Atlas-Based Segmentation,
MedImg(34), No. 12, December 2015, pp. 2492-2505.
IEEE DOI 1601
Bayes methods BibRef

Iglesias, J.E.[Juan Eugenio], Sabuncu, M.R.[Mert Rory], van Leemput, K.[Koen],
A Generative Model for Probabilistic Label Fusion of Multimodal Data,
MBIA12(115-133).
Springer DOI 1210
BibRef

Sabuncu, M.R., van Leemput, K.,
The Relevance Voxel Machine (RVoxM): A Self-Tuning Bayesian Model for Informative Image-Based Prediction,
MedImg(31), No. 12, December 2012, pp. 2290-2306.
IEEE DOI 1212
BibRef

Li, T.[Teng], Yan, S.C.[Shui-Cheng], Mei, T.[Tao], Hua, X.S.[Xian-Sheng], Kweon, I.S.[In-So],
Image Decomposition With Multilabel Context: Algorithms and Applications,
IP(20), No. 8, August 2011, pp. 2301-2314.
IEEE DOI 1108
Segmentation. Context from multiple images. BibRef

Chen, X.Y.[Xiang-Yu], Yuan, X.T.[Xiao-Tong], Chen, Q.A.[Qi-Ang], Yan, S.C.[Shui-Cheng], Chua, T.S.[Tat-Seng],
Multi-label visual classification with label exclusive context,
ICCV11(834-841).
IEEE DOI 1201
BibRef

Li, T.[Teng], Mei, T.[Tao], Yan, S.C.[Shui-Cheng], Kweon, I.S.[In-So], Lee, C.W.[Chil-Woo],
Contextual decomposition of multi-label images,
CVPR09(2270-2277).
IEEE DOI 0906
Context for segmentation. BibRef

Scheffler, C.[Carl], Odobez, J.M.[Jean-Marc],
Joint Adaptive Colour Modelling and Skin, Hair and Clothes Segmentation using Coherent Probabilistic Index Maps,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Zheng, W.S.[Wei-Shi], Gong, S.G.[Shao-Gang], Xiang, T.[Tao],
Quantifying and Transferring Contextual Information in Object Detection,
PAMI(34), No. 4, April 2012, pp. 762-777.
IEEE DOI 1203
BibRef
Earlier:
Unsupervised Selective Transfer Learning for Object Recognition,
ACCV10(II: 527-541).
Springer DOI 1011
BibRef
Earlier:
Quantifying contextual information for object detection,
ICCV09(932-939).
IEEE DOI 0909
Context model without prior segmentation of context annotation.
See also Learning Behavioural Context. BibRef

Liu, S., Yan, S., Zhang, T., Xu, C., Liu, J., Lu, H.,
Weakly Supervised Graph Propagation Towards Collective Image Parsing,
MultMed(14), No. 2, 2012, pp. 361-373.
IEEE DOI 1204
Assign annotated labels to contextually derived semantic regions. BibRef

Alush, A.[Amir], Goldberger, J.[Jacob],
Ensemble Segmentation Using Efficient Integer Linear Programming,
PAMI(34), No. 10, October 2012, pp. 1966-1977.
IEEE DOI 1208
Combine several segmentations into one. BibRef

Reinbacher, C., Rüther, M., Bischof, H.,
Fast variational multi-view segmentation through backprojection of spatial constraints,
IVC(30), No. 11, November 2012, pp. 797-807.
Elsevier DOI 1211
Multi-view; Variational segmentation; GPU; Catadioptric BibRef

Kim, S., Nowozin, S., Kohli, P., Yoo, C.D.,
Task-Specific Image Partitioning,
IP(22), No. 2, February 2013, pp. 488-500.
IEEE DOI 1302
BibRef

Kim, S., Yoo, C.D., Nowozin, S., Kohli, P.,
Image Segmentation Using Higher-Order Correlation Clustering,
PAMI(36), No. 9, September 2014, pp. 1761-1774.
IEEE DOI 1408
Clustering algorithms BibRef

Liu, Y.C.[Yen-Cheng], Chen, H.T.[Hwann-Tzong],
Unsupervised scene segmentation using sparse coding context,
MVA(24), No. 2, February 2013, pp. 243-254.
WWW Link. 1302
Semantically meaningful regions without user annotation. BibRef

Chung, F.[Francois], Delingette, H.[Herve],
Regional appearance modeling based on the clustering of intensity profiles,
CVIU(117), No. 6, June 2013, pp. 705-717.
Elsevier DOI 1304
Appearance modeling; Unsupervised clustering; Model-based image segmentation; Medical imaging BibRef

Chen, Q.A.[Qi-Ang], He, C.J.[Chuan-Jiang],
Variational segmentation model for images with intensity inhomogeneity and Poisson noise,
JIVP(2013), No. 1, 2013, pp. xx-yy.
DOI Link 1306
BibRef

Chen, Q.A.[Qi-Ang], He, C.J.[Chuan-Jiang],
Integrating clustering with level set method for piecewise constant Mumford-Shah model,
JIVP(2014), No. 1, 2014, pp. 1.
DOI Link 1402
BibRef

Zhou, Q.[Quan], Zhu, J.[Jun], Liu, W.Y.[Wen-Yu],
Learning Dynamic Hybrid Markov Random Field for Image Labeling,
IP(22), No. 6, 2013, pp. 2219-2232.
IEEE DOI 1307
Markov processes; tree searching; Deformable models; Histograms BibRef

Zhou, Q.[Quan], Yan, C.X.[Can-Xiang], Zhu, Y.Y.[Ying-Ying], Bai, X.[Xiang], Liu, W.Y.[Wen-Yu],
Image labeling by multiple segmentation,
ICIP11(3129-3132).
IEEE DOI 1201
BibRef

Zhou, Q.[Quan], Liu, W.Y.[Wen-Yu],
Inference Scene Labeling by Incorporating Object Detection with Explicit Shape Model,
ACCV10(III: 382-395).
Springer DOI 1011
Shape, texture and context. Segment and label the regions. BibRef

Khan, A.A.[Asmar Azar], Xydeas, C.[Costas], Ahmed, H.[Hassan],
Using macroscopic information in image segmentation,
IET-IPR(7), No. 3, 2013, pp. 219-228.
DOI Link 1307
BibRef

Zhou, Y.B.[Ying-Bo], Nwogu, I.[Ifeoma], Govindaraju, V.[Venu],
Labeling Spain With Stanford,
IP(22), No. 12, 2013, pp. 5362-5371.
IEEE DOI 1312
computer vision Outdoor scene decomposition, use the Stanford background data set. BibRef

Nwogu, I.[Ifeoma], Govindaraju, V.[Venu], Brown, C.[Chris],
Syntactic image parsing using ontology and semantic descriptions,
POCV10(41-48).
IEEE DOI 1006
Semantic descriptions, real-world spatial relationships, colors, textures, shape. BibRef

Waggoner, J.[Jarrell], Zhou, Y.J.[You-Jie], Simmons, J.[Jeff], de Graef, M.[Marc], Wang, S.[Song],
3D Materials Image Segmentation by 2D Propagation: A Graph-Cut Approach Considering Homomorphism,
IP(22), No. 12, 2013, pp. 5282-5293.
IEEE DOI 1312
graph theory BibRef

Waggoner, J.[Jarrell], Zhou, Y.J.[You-Jie], Simmons, J.[Jeff], de Graef, M.[Marc], Wang, S.[Song],
Topology-Preserving Multi-label Image Segmentation,
WACV15(1084-1091)
IEEE DOI 1503
Cost function BibRef

Luo, B.[Bing], Huang, C.[Chao], Ma, L.[Lei], Li, W.[Wei], Wu, Q.B.[Qing-Bo],
Foreground Segmentation via Dynamic Programming,
IEICE(E97-D), No. 10, October 2014, pp. 2818-2822.
WWW Link. 1411
Segment object of specific class based on deformable part model. BibRef

Luo, B.[Bing], Li, H., Meng, F., Wu, Q.B.[Qing-Bo], Huang, C.,
Video Object Segmentation via Global Consistency Aware Query Strategy,
MultMed(19), No. 7, July 2017, pp. 1482-1493.
IEEE DOI 1706
History, Manuals, Motion segmentation, Object segmentation, Proposals, Tracking, Video sequences, Energy minimization, global consistency, video, object, segmentation BibRef

Luo, B.[Bing], Li, H.L.[Hong-Liang], Meng, F.M.[Fan-Man], Wu, Q.B.[Qing-Bo], Ngan, K.N.[King N.],
An Unsupervised Method to Extract Video Object via Complexity Awareness and Object Local Parts,
CirSysVideo(28), No. 7, July 2018, pp. 1580-1594.
IEEE DOI 1807
Complexity theory, Image segmentation, Motion segmentation, Object segmentation, Proposals, Streaming media, Video sequences, video segmentation BibRef

Rezvanifar, A., Khosravifard, M.,
Including the Size of Regions in Image Segmentation by Region-Based Graph,
IP(23), No. 2, February 2014, pp. 635-644.
IEEE DOI 1402
computational complexity BibRef

He, H.[Hu], Upcroft, B.,
Automatic object segmentation of unstructured scenes using colour and depth maps,
IET-CV(8), No. 1, February 2014, pp. 45-53.
DOI Link 1404
Markov processes BibRef

You, X.G.[Xin-Ge], Li, Q.A.[Qi-Ang], Tao, D.C.[Da-Cheng], Ou, W.H.[Wei-Hua], Gong, M.M.[Ming-Ming],
Local Metric Learning for Exemplar-Based Object Detection,
CirSysVideo(24), No. 8, August 2014, pp. 1265-1276.
IEEE DOI 1410
computer vision BibRef

Liu, Y.M.[Yong-Mei], Wongwitit, T.[Tanakrit], Yu, L.[Linsen],
Automatic Image Annotation Based on Scene Analysis,
IJIG(14), No. 03, 2014, pp. 1450012.
DOI Link 1410
BibRef

Sanroma, G.[Gerard], Wu, G.R.[Guo-Rong], Gao, Y.Z.[Yao-Zong], Shen, D.G.[Ding-Gang],
Learning to Rank Atlases for Multiple-Atlas Segmentation,
MedImg(33), No. 10, October 2014, pp. 1939-1953.
IEEE DOI 1411
BibRef
And: Correction: MedImg(33), No. 11, November 2014, pp. 2210-2210.
IEEE DOI 1411
BibRef
Earlier:
Learning-Based Atlas Selection for Multiple-Atlas Segmentation,
CVPR14(3111-3117)
IEEE DOI 1409
Atlas selection. image segmentation. Biomedical imaging. BibRef

Nie, D.[Dong], Shen, D.G.[Ding-Gang],
Adversarial Confidence Learning for Medical Image Segmentation and Synthesis,
IJCV(128), No. 10-11, November 2020, pp. 2494-2513.
Springer DOI 2009
BibRef

Guillaumin, M.[Matthieu], Küttel, D.[Daniel], Ferrari, V.[Vittorio],
ImageNet Auto-Annotation with Segmentation Propagation,
IJCV(110), No. 1, December 2014, pp. 328-348.
Springer DOI 1411
BibRef
Earlier: A2, A1, A3:
Segmentation Propagation in ImageNet,
ECCV12(VII: 459-473).
Springer DOI 1210
Award, ECCV. BibRef

Kuettel, D.[Daniel], Ferrari, V.[Vittorio],
Learning to approximate global shape priors for figure-ground segmentation,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Seyedhosseini, M.[Mojtaba], Tasdizen, T.[Tolga],
Disjunctive normal random forests,
PR(48), No. 3, 2015, pp. 976-983.
Elsevier DOI 1412
Random forest BibRef

Sajjadi, M.[Mehdi], Seyedhosseini, M.[Mojtaba], Tasdizen, T.[Tolga],
Nonlinear Regression with Logistic Product Basis Networks,
SPLetters(22), No. 8, August 2015, pp. 1011-1015.
IEEE DOI 1502
BibRef
Earlier: A2, A1, A3:
Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks,
ICCV13(2168-2175)
IEEE DOI 1403
Contextual information. regression analysis. BibRef

Seyedhosseini, M.[Mojtaba], Tasdizen, T.[Tolga],
Semantic Image Segmentation with Contextual Hierarchical Models,
PAMI(38), No. 5, May 2016, pp. 951-964.
IEEE DOI 1604
edge detection BibRef

Liu, T., Seyedhosseini, M.[Mojtaba], Tasdizen, T.[Tolga],
Image Segmentation Using Hierarchical Merge Tree,
IP(25), No. 10, October 2016, pp. 4596-4607.
IEEE DOI 1610
image classification BibRef

Kim, J.[Jaechul], Grauman, K.[Kristen],
Boundary Preserving Dense Local Regions,
PAMI(37), No. 5, May 2015, pp. 931-943.
IEEE DOI 1504
BibRef
Earlier: CVPR11(1553-1560).
IEEE DOI 1106
Detectors BibRef

Lee, Y.J.[Yong Jae], Grauman, K.[Kristen],
Collect-cut: Segmentation with top-down cues discovered in multi-object images,
CVPR10(3185-3192).
IEEE DOI 1006
BibRef

Lee, Y.J.[Yong Jae], Kim, J.[Jaechul], Grauman, K.[Kristen],
Key-segments for video object segmentation,
ICCV11(1995-2002).
IEEE DOI 1201
Find possible objects, then find ones with persistent appearance and motion.
See also Foreground Focus: Unsupervised Learning from Partially Matching Images. BibRef

Vijayanarasimhan, S.[Sudheendra], Grauman, K.[Kristen],
Top-down pairwise potentials for piecing together multi-class segmentation puzzles,
POCV10(25-32).
IEEE DOI 1006
BibRef

Gao, Y.[Yi], Zhu, L.J.[Liang-Jia], Cates, J.[Joshua], MacLeod, R.S.[Rob S.], Bouix, S.[Sylvain], Tannenbaum, A.[Allen],
A Kalman Filtering Perspective for Multiatlas Segmentation,
SIIMS(8), No. 2, 2015, pp. 1007-1029.
DOI Link 1507
BibRef

Djelouah, A.[Abdelaziz], Franco, J.S.[Jean-Sébastien], Boyer, E.[Edmond], Le Clerc, F.[François], Pérez, P.[Patrick],
Sparse Multi-View Consistency for Object Segmentation,
PAMI(37), No. 9, September 2015, pp. 1890-1903.
IEEE DOI 1508
BibRef
Earlier:
Multi-view Object Segmentation in Space and Time,
ICCV13(2640-2647)
IEEE DOI 1403
BibRef
Earlier:
N-tuple Color Segmentation for Multi-View Silhouette Extraction,
ECCV12(V: 818-831).
Springer DOI 1210
Cameras. multi-view segmentation; segmentation BibRef

Djelouah, A.[Abdelaziz], Franco, J.S.[Jean-Sébastien], Boyer, E.[Edmond], Pérez, P.[Patrick], Drettakis, G.,
Cotemporal Multi-View Video Segmentation,
3DV16(360-369)
IEEE DOI 1701
Calibration BibRef

Ye, L., Liu, Z., Zhou, X., Shen, L., Zhang, J.,
Saliency Detection Via Similar Image Retrieval,
SPLetters(23), No. 6, June 2016, pp. 838-842.
IEEE DOI 1606
Boosting. Propogate salience from similar images. BibRef

Zhou, Q.[Quan], Zheng, B.Y.[Bao-Yu], Zhu, W.P.[Wei-Ping], Latecki, L.J.[Longin Jan],
Multi-scale context for scene labeling via flexible segmentation graph,
PR(59), No. 1, 2016, pp. 312-324.
Elsevier DOI 1609
Scene labeling BibRef

Zhang, W.[Wei], Ngo, C.W.[Chong-Wah], Cao, X.C.[Xiao-Chun],
Hyperlink-Aware Object Retrieval,
IP(25), No. 9, September 2016, pp. 4186-4198.
IEEE DOI 1609
data mining BibRef

Liu, F.Y.[Fa-Yao], Lin, G.S.[Guo-Sheng], Shen, C.H.[Chun-Hua],
Discriminative Training of Deep Fully Connected Continuous CRFs With Task-Specific Loss,
IP(26), No. 5, May 2017, pp. 2127-2136.
IEEE DOI 1704
Labeling Conditional Random Fields. BibRef

Cordts, M., Rehfeld, T., Enzweiler, M.[Markus], Franke, U.[Uwe], Roth, S.[Stefan],
Tree-Structured Models for Efficient Multi-Cue Scene Labeling,
PAMI(39), No. 7, July 2017, pp. 1444-1454.
IEEE DOI 1706
Detectors, Feature extraction, Histograms, Labeling, Proposals, Semantics, Vegetation, Scene labeling, automotive, decision forests, depth cues, segmentation tree, stixels, superpixels BibRef

Scharwächter, T.[Timo], Enzweiler, M.[Markus], Franke, U.[Uwe], Roth, S.[Stefan],
Efficient Multi-cue Scene Segmentation,
GCPR13(435-445).
Springer DOI 1311
Award, GCPR. BibRef

Scharwächter, T.[Timo],
Stixel-Based Target Existence Estimation under Adverse Conditions,
GCPR13(225-230).
Springer DOI 1311
BibRef

Dann, C.[Christoph], Gehler, P.V.[Peter V.], Roth, S.[Stefan], Nowozin, S.[Sebastian],
Pottics: The Potts Topic Model for Semantic Image Segmentation,
DAGM12(397-407).
Springer DOI 1209
BibRef

Liu, C.X.[Chun-Xiao], Ng, M.K.P.[Michael Kwok-Po], Zeng, T.Y.[Tie-Yong],
Weighted variational model for selective image segmentation with application to medical images,
PR(76), No. 1, 2018, pp. 367-379.
Elsevier DOI 1801
Selective segmentation BibRef

Wang, Z., Yuan, J.,
Simultaneously Discovering and Localizing Common Objects in Wild Images,
IP(27), No. 9, September 2018, pp. 4503-4515.
IEEE DOI 1807
feature extraction, graph theory, image classification, image representation, image retrieval, image segmentation, unsupervised learning BibRef

Zhang, Y.Q.[Yong-Qiang], Bai, Y.C.[Yai-Cheng], Ding, M.L.[Ming-Li], Li, Y.Q.[Yong-Qiang], Ghanem, B.[Bernard],
Weakly-supervised object detection via mining pseudo ground truth bounding-boxes,
PR(84), 2018, pp. 68-81.
Elsevier DOI 1809
BibRef
And: Corrigendum: PR(90), 2019, pp. 483.
Elsevier DOI 1903
Weakly-supervised learning, Object detection, Pseudo ground truth, Iterative learning, Deep learning BibRef

Zhang, Y.Q.[Yong-Qiang], Ding, M.L.[Ming-Li], Bai, Y.C.[Yan-Cheng], Xu, M.M.[Meng-Meng], Ghanem1, B.[Bernard],
Beyond Weakly Supervised: Pseudo Ground Truths Mining for Missing Bounding-Boxes Object Detection,
CirSysVideo(30), No. 4, April 2020, pp. 983-997.
IEEE DOI 2004
Object detection, Detectors, Training data, Benchmark testing, Task analysis, Visualization, Semantics, Pseudo ground truths, missing bounding-boxes BibRef

Aliniya, P.[Parvaneh], Razzaghi, P.[Parvin],
Parametric and nonparametric context models: A unified approach to scene parsing,
PR(84), 2018, pp. 165-181.
Elsevier DOI 1809
Scene parsing, Parametric, Nonparametric, Context model, Co-occurrence graph, Unified framework BibRef

Zhou, B.L.[Bo-Lei], Zhao, H.[Hang], Puig, X.[Xavier], Xiao, T.[Tete], Fidler, S.[Sanja], Barriuso, A.[Adela], Torralba, A.B.[Antonio B.],
Semantic Understanding of Scenes Through the ADE20K Dataset,
IJCV(127), No. 3, March 2019, pp. 302-321.
Springer DOI 1903
BibRef
Earlier: A1, A2, A3, A5, A6, A7, Only:
Scene Parsing through ADE20K Dataset,
CVPR17(5122-5130)
IEEE DOI
PDF File. 1711
Image segmentation, Labeling, Neural networks, Semantics, Sun, Visualization
See also ADE20K Dataset. BibRef

Zhao, H.[Hang], Puig, X.[Xavier], Zhou, B.L.[Bo-Lei], Fidler, S.[Sanja], Torralba, A.B.[Antonio B.],
Open Vocabulary Scene Parsing,
ICCV17(2021-2029)
IEEE DOI 1802
document image processing, grammars, vocabulary, ADE20K dataset, Open Vocabulary Scene Parsing, image pixel, Vocabulary BibRef

Jiang, S., Liang, S., Chen, C., Zhu, Y., Li, X.,
Class Agnostic Image Common Object Detection,
IP(28), No. 6, June 2019, pp. 2836-2846.
IEEE DOI 1905
feature extraction, image classification, image matching, learning (artificial intelligence), relation network BibRef

Lei, T., Jia, X., Liu, T., Liu, S., Meng, H., Nandi, A.K.,
Adaptive Morphological Reconstruction for Seeded Image Segmentation,
IP(28), No. 11, November 2019, pp. 5510-5523.
IEEE DOI 1909
Image segmentation, Image reconstruction, Image edge detection, Transforms, Convergence, Morphology, Computer science, spectral segmentation BibRef

Lee, M.C.H., Petersen, K., Pawlowski, N., Glocker, B., Schaap, M.,
TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors,
MedImg(38), No. 11, November 2019, pp. 2596-2606.
IEEE DOI 1911
Shape, Neural networks, Image segmentation, Strain, Deformable models, Training, Task analysis, Image segmentation, image registration BibRef

Sharma, A.[Ashu], Ghosh, J.K.[Jayanta Kumar], Kolay, S.[Saptarshi],
Reference data preparation for complex satellite image segmentation,
IET-IPR(14), No. 4, 27 March 2020, pp. 628-637.
DOI Link 2003
Training data for segmentation. BibRef

Qiu, H.Q.[He-Qian], Li, H.L.[Hong-Liang], Wu, Q.B.[Qing-Bo], Meng, F.M.[Fan-Man], Xu, L.F.[Lin-Feng], Ngan, K.N.[King Ngi], Shi, H.C.[Heng-Can],
Hierarchical Context Features Embedding for Object Detection,
MultMed(22), No. 12, December 2020, pp. 3039-3050.
IEEE DOI 2011
Feature extraction, Object detection, Logic gates, Image segmentation, Convolution, Semantics, Decoding, gated encoder-decoder network BibRef

Zhou, F.[Feng], Hang, R.L.[Ren-Long], Liu, Q.S.[Qing-Shan],
Class-Guided Feature Decoupling Network for Airborne Image Segmentation,
GeoRS(59), No. 3, March 2021, pp. 2245-2255.
IEEE DOI 2103
Feature extraction, Image segmentation, Automobiles, Semantics, Context modeling, Task analysis, Computer architecture, semantic segmentation BibRef

Chen, Z.M.[Zhao-Min], Jin, X.[Xin], Zhao, B.R.[Bo-Rui], Zhang, X.Q.[Xiao-Qin], Guo, Y.[Yanwen],
HCE: Hierarchical Context Embedding for Region-Based Object Detection,
IP(30), 2021, pp. 6917-6929.
IEEE DOI 2108
Detectors, Feature extraction, Proposals, Object detection, Head, Training, Noise measurement, Object detection, context embedding, region-based CNNs BibRef

Chen, Z.M.[Zhao-Min], Jin, X.[Xin], Zhao, B.R.[Bo-Rui], Wei, X.S.[Xiu-Shen], Guo, Y.[Yanwen],
Hierarchical Context Embedding for Region-based Object Detection,
ECCV20(XXI:633-648).
Springer DOI 2011
BibRef

Tomar, D.[Devavrat], Bozorgtabar, B.[Behzad], Vray, M.L.G.[Manana Lortkipanidze Guillaume], Rad, M.S.[Mohammad Saeed], Thiran, J.P.[Jean-Philippe],
Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation,
WACV22(1737-1747)
IEEE DOI 2202
Training, Image segmentation, Computational modeling, Magnetic resonance imaging, Transfer learning, Brain modeling, Semi- and Un- supervised Learning BibRef

Xu, Y.Z.[Yu-Zheng], Wu, Y.[Yang], Zuraimi, N.S.B.[Nur Sabrina Binti], Nobuhara, S.[Shohei], Nishino, K.[Ko],
Video Region Annotation with Sparse Bounding Boxes,
IJCV(131), No. 3, March 2023, pp. 717-731.
Springer DOI 2302
automatically generate region boundaries for all frames of a video from sparsely annotated bounding boxes of target regions BibRef

Qu, H.C.[Hai-Cheng], Wang, X.N.[Xiao-Na], Wang, Y.[Ying], Chen, Y.[Yao],
Multi-branch residual image semantic segmentation combined with inverse weight gated-control,
IVC(143), 2024, pp. 104932.
Elsevier DOI 2403
Image semantic segmentation, Deep multi-branch residuals, Attention, Inverse weight gated-control, Contextual information BibRef

Xing, P.[Peng], Sun, Y.P.[Yan-Peng], Zeng, D.[Dan], Li, Z.C.[Ze-Chao],
Normal Image Guided Segmentation Framework for Unsupervised Anomaly Detection,
CirSysVideo(34), No. 6, June 2024, pp. 4639-4652.
IEEE DOI 2406
Feature extraction, Image segmentation, Anomaly detection, Training, Image reconstruction, Task analysis, feature guidance BibRef

Wang, X.L.[Xin-Long], Zhang, X.S.[Xiao-Song], Cao, Y.[Yue], Wang, W.[Wen], Shen, C.H.[Chun-Hua], Huang, T.J.[Tie-Jun],
SegGPT: Towards Segmenting Everything In Context,
ICCV23(1130-1140)
IEEE DOI 2401
BibRef

Okazawa, A.[Atsuro],
Interclass Prototype Relation for Few-Shot Segmentation,
ECCV22(XXIX:362-378).
Springer DOI 2211
BibRef

Yu, J.[Jinan], Ma, L.Y.[Li-Yan], Li, Z.L.[Zheng-Lin], Peng, Y.[Yan], Xie, S.R.[Shao-Rong],
Open-World Object Detection via Discriminative Class Prototype Learning,
ICIP22(626-630)
IEEE DOI 2211
Training, Measurement, Image coding, Prototypes, Object detection, Benchmark testing, Object recognition, Embedding Space Compressor BibRef

Saha, O.[Oindrila], Cheng, Z.[Zezhou], Maji, S.[Subhransu],
GANORCON: Are Generative Models Useful for Few-shot Segmentation?,
CVPR22(9981-9990)
IEEE DOI 2210
Training, Representation learning, Image segmentation, Shape, Training data, Self-supervised learning, grouping and shape analysis BibRef

Liu, S.[Sheng], Liu, K.N.[Kang-Ning], Zhu, W.C.[Wei-Cheng], Shen, Y.Q.[Yi-Qiu], Fernandez-Granda, C.[Carlos],
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations,
CVPR22(2596-2606)
IEEE DOI 2210
Code, Segmentation.
WWW Link. Training, Deep learning, Annotations, Shape, Semantics, Robustness, Pattern recognition, Segmentation, grouping and shape analysis BibRef

Workman, S.[Scott], Rafique, M.U.[M. Usman], Blanton, H.[Hunter], Jacobs, N.[Nathan],
Revisiting Near/Remote Sensing with Geospatial Attention,
CVPR22(1768-1777)
IEEE DOI 2210
Overhead image segmentation with ground-level images. Image segmentation, Social networking (online), Computational modeling, Computer architecture, Vision + X BibRef

Chen, C.S.[Chen-Shu], Liu, T.[Tao], Tan, W.M.[Wen-Ming], Pu, S.L.[Shi-Liang],
Mixed-Dual-Head Meets Box Priors: A Robust Framework for Semi-supervised Segmentation,
WACV22(2592-2602)
IEEE DOI 2202
Training, Image segmentation, Annotations, Semantics, Interference, Segmentation, Semi- and Un- supervised Learning BibRef

Chowdhury, S.[Sanjoy], Dasgupta, S.[Subhrajyoti], Das, S.[Sudip], Bhattacharya, U.[Ujjwal],
Listen To the Pixels,
ICIP21(2568-2572)
IEEE DOI 2201
Sound source separation and visual object segmentation. Location awareness, Concurrent computing, Visualization, Source separation, Semantics, Object segmentation, partial occlusion BibRef

Niloy, F.F.[Fahim Faisal], Amin, M.A.[M. Ashraful], Ali, A.A.[Amin Ahsan], Rahman, A.K.M.M.[A.K.M. Mahbubur],
Attention Toward Neighbors: A Context Aware Framework for High Resolution Image Segmentation,
ICIP21(2279-2283)
IEEE DOI 2201
Context-aware services, Image segmentation, Image resolution, Fuses, Task analysis, High-resolution image segmentation, Contextual information BibRef

Azam, B., Mandal, R., Zhang, L., Verma, B.,
Class Probability-based Visual and Contextual Feature Integration for Image Parsing,
IVCNZ20(1-6)
IEEE DOI 2012
Training, Deep learning, Visualization, Image segmentation, Computer architecture, Benchmark testing, Task analysis, deep learning BibRef

Wu, T.Y.[Tian-Yi], Lu, Y.[Yu], Zhu, Y.[Yu], Zhang, C.[Chuang], Wu, M.[Ming], Ma, Z.Y.[Zhan-Yu], Guo, G.D.[Guo-Dong],
GiNet: Graph Interaction Network for Scene Parsing,
ECCV20(XVII:34-51).
Springer DOI 2011
Incorperate the linguistic knowledge to promote context reasoning. BibRef

Zhong, Y.J.[Yu-Jie], Deng, Z.[Zelu], Guo, S.[Sheng], Scott, M.R.[Matthew R.], Huang, W.L.[Wei-Lin],
Representation Sharing for Fast Object Detector Search and Beyond,
ECCV20(XIX:471-487).
Springer DOI 2011
BibRef

Mendel, R.[Robert], de Souza, Jr., L.A.[Luis Antonio], Rauber, D.[David], Papa, J.P.[João Paulo], Palm, C.[Christoph],
Semi-supervised Segmentation Based on Error-correcting Supervision,
ECCV20(XXIX: 141-157).
Springer DOI 2010
BibRef

Yu, C., Wang, J., Gao, C., Yu, G., Shen, C., Sang, N.,
Context Prior for Scene Segmentation,
CVPR20(12413-12422)
IEEE DOI 2008
Aggregates, Semantics, Context modeling, Image segmentation, Feature extraction, Task analysis, Encoding BibRef

Ortiz, A., Robinson, C., Morris, D., Fuentes, O., Kiekintveld, C., Hassan, M.M., Jojic, N.,
Local Context Normalization: Revisiting Local Normalization,
CVPR20(11273-11282)
IEEE DOI 2008
Neural networks, Convergence, Image segmentation, Training BibRef

McIntosh, B.[Bruce], Duarte, K.[Kevin], Rawat, Y.S.[Yogesh S.], Shah, M.[Mubarak],
Visual-Textual Capsule Routing for Text-Based Video Segmentation,
CVPR20(9939-9948)
IEEE DOI 2008
Actor and action video segmentation from a sentence. Routing, Visualization, Task analysis, Natural languages, Feature extraction, Convolution, Merging BibRef

Yang, K., Zhang, P., Qiao, P., Wang, Z., Dai, H., Shen, T., Li, D., Dou, Y.,
Rethinking Segmentation Guidance for Weakly Supervised Object Detection,
VL3W20(4069-4073)
IEEE DOI 2008
Proposals, Image segmentation, Optical character recognition software, Detectors, Semantics, Object detection BibRef

Yang, Z.H.[Zhen-Heng], Mahajan, D.[Dhruv], Ghadiyaram, D.[Deepti], Nevatia, R.[Ram], Ramanathan, V.[Vignesh],
Activity Driven Weakly Supervised Object Detection,
CVPR19(2912-2921).
IEEE DOI 2002
BibRef

Dmitriev, K.[Konstantin], Kaufman, A.E.[Arie E.],
Learning Multi-Class Segmentations From Single-Class Datasets,
CVPR19(9493-9503).
IEEE DOI 2002
BibRef

Ding, H.[Henghui], Jiang, X.D.[Xu-Dong], Shuai, B.[Bing], Liu, A.Q.[Ai Qun], Wang, G.[Gang],
Semantic Correlation Promoted Shape-Variant Context for Segmentation,
CVPR19(8877-8886).
IEEE DOI 2002
BibRef

Kim, Y.[Yonghyun], Kim, T.[Taewook], Kang, B.N.[Bong-Nam], Kim, J.[Jieun], Kim, D.J.[Dai-Jin],
BAN: Focusing on Boundary Context for Object Detection,
ACCV18(VI:555-570).
Springer DOI 1906
BibRef

Zhao, Q.J.[Qi-Jie], Wang, Y.T.[Yong-Tao], Sheng, T.[Tao], Tang, Z.[Zhi],
Comprehensive Feature Enhancement Module for Single-Shot Object Detector,
ACCV18(V:325-340).
Springer DOI 1906
BibRef

Rao, C.[Cong], Fan, Y.[Yi], Su, K.L.[Kai-Le], Latecki, L.J.[Longin Jan],
Common Object Discovery as Local Search for Maximum Weight Cliques in a Global Object Similarity Graph,
DGCI19(219-233).
Springer DOI 1905
BibRef

Pino, O.[Omar], Nascimento, E.[Erickson], Campos, M.[Mario],
Prototypicality Effects in Global Semantic Description of Objects,
WACV19(1233-1242)
IEEE DOI 1904
convolutional neural nets, feature extraction, image classification, learning (artificial intelligence), Organizations BibRef

Lu, H.F.[Hsueh-Fu], Du, X.F.[Xiao-Fei], Chang, P.L.[Ping-Lin],
Toward Scale-Invariance and Position-Sensitive Region Proposal Networks,
ECCV18(VIII: 175-190).
Springer DOI 1810
BibRef

Singh, K.K.[Krishna Kumar], Divvala, S.[Santosh], Farhadi, A.[Ali], Lee, Y.J.[Yong Jae],
DOCK: Detecting Objects by Transferring Common-Sense Knowledge,
ECCV18(XIII: 506-522).
Springer DOI 1810
BibRef

Liu, S.F.[Si-Fei], Zhong, G.Y.[Guang-Yu], de Mello, S.[Shalini], Gu, J.W.[Jin-Wei], Jampani, V.[Varun], Yang, M.H.[Ming-Hsuan], Kautz, J.[Jan],
Switchable Temporal Propagation Network,
ECCV18(VII: 89-104).
Springer DOI 1810
Apply to colorizing a video based on a few key-frames, generating an HDR video, and propagating a segmentation mask from the first frame in videos. BibRef

Wang, Y.F.[Yu-Fei], Lin, Z.[Zhe], Shen, X.H.[Xiao-Hui], Zhang, J.M.[Jian-Ming], Cohen, S.[Scott],
Concept Mask: Large-Scale Segmentation from Semantic Concepts,
ECCV18(XII: 542-557).
Springer DOI 1810
BibRef

Holuša, M.[Michael], Sojka, E.[Eduard],
On the Stability of the K-Max Distance to the Position of Seeds,
ICIP18(261-265)
IEEE DOI 1809
Image segmentation, Image edge detection, Transforms, Level measurement, Standards, Q measurement, Distance transform, Image segmentation BibRef

Jin, B.[Bin], Ortiz Segovia, M.V.[Maria V.], Süsstrunk, S.[Sabine],
Webly Supervised Semantic Segmentation,
CVPR17(1705-1714)
IEEE DOI 1711
Weakly supervised from web tags. Benchmark testing, Image segmentation, Neural networks, Pipelines, Semantics, Training, Visualization BibRef

Xu, Y.[Yang], Li, J.[Jun], Chen, J.B.[Jian-Bin], Shen, G.T.[Guang-Tian], Gao, Y.J.[Yang-Jian],
A novel approach for visual Saliency detection and segmentation based on objectness and top-down attention,
ICIVC17(361-365)
IEEE DOI 1708
Algorithm design and analysis, Benchmark testing, Computational modeling, Detectors, Image recognition, Image segmentation, object segmentation, objectness, saliency detection, visual attention BibRef

Hu, R.H.[Rong-Hang], Rohrbach, M.[Marcus], Darrell, T.J.[Trevor J.],
Segmentation from Natural Language Expressions,
ECCV16(I: 108-124).
Springer DOI 1611
BibRef

Lee, C.K., Liu, T.L.,
Guided co-training for multi-view spectral clustering,
ICIP16(4042-4046)
IEEE DOI 1610
Algorithm design and analysis BibRef

Acuña, R.G.G.[Rafael Guillermo Gonzalez], Tao, J.L.[Jun-Li], Klette, R.[Reinhard],
Generalization of Otsu's binarization into recursive colour image segmentation,
ICVNZ15(1-6)
IEEE DOI 1701
image colour analysis BibRef

Acuña, R.G.G.[Rafael Guillermo Gonzalez], Tao, J.L.[Jun-Li], Breen, D.[Daniel], Breen, B.[Barbara], Pointing, S.[Steve], Gillman, L.[Len], Klette, R.[Reinhard],
Robust Segmentation of Aerial Image Data Recorded for Landscape Ecology Studies,
GPID15(61-72).
Springer DOI 1603
BibRef

Xiao, F.[Fanyi], Lee, Y.J.[Yong Jae],
Discovering the Spatial Extent of Relative Attributes,
ICCV15(1458-1466)
IEEE DOI 1602
Computational modeling BibRef

Song, Y.H.[Yi-Hua], Gong, Z.X.[Zhao-Xuan], Zhao, D.[Dazhe], Feng, C.[Chaolu], Li, C.M.[Chun-Ming],
Impact of the Number of Atlases in a Level Set Formulation of Multi-atlas Segmentation,
ISVC15(I: 531-537).
Springer DOI 1601
BibRef

Shimoda, W.[Wataru], Yanai, K.[Keiji],
CNN-Based Food Image Segmentation Without Pixel-Wise Annotation,
MADiMa15(449-457).
Springer DOI 1511
BibRef

Dehais, J.[Joachim], Anthimopoulos, M.[Marios], Mougiakakou, S.[Stavroula],
Dish Detection and Segmentation for Dietary Assessment on Smartphones,
MADiMa15(433-440).
Springer DOI 1511
BibRef

Pinheiro, P.O.[Pedro O.], Lin, T.Y.[Tsung-Yi], Collobert, R.[Ronan], Dollár, P.[Piotr],
Learning to Refine Object Segments,
ECCV16(I: 75-91).
Springer DOI 1611
BibRef

Pinheiro, P.O.[Pedro O.], Collobert, R.[Ronan],
From image-level to pixel-level labeling with Convolutional Networks,
CVPR15(1713-1721)
IEEE DOI 1510
BibRef

George, M.[Marian],
Image parsing with a wide range of classes and scene-level context,
CVPR15(3622-3630)
IEEE DOI 1510
BibRef

Lee, S.H., Jang, W.D., Park, B.K., Kim, C.S.,
RGB-D image segmentation based on multiple random walkers,
ICIP16(2549-2553)
IEEE DOI 1610
Clustering algorithms BibRef

Lee, C.[Chulwoo], Jang, W.D.[Won-Dong], Sim, J.Y.[Jae-Young], Kim, C.S.[Chang-Su],
Multiple random walkers and their application to image cosegmentation,
CVPR15(3837-3845)
IEEE DOI 1510
BibRef

Kasiri, K.[Keyvan], Fieguth, P.W.[Paul W.], Clausi, D.A.[David A.],
Cross modality label fusion in multi-atlas segmentation,
ICIP14(16-20)
IEEE DOI 1502
Accuracy BibRef

Zhang, F.Q.[Fei-Qian], Sun, Z.X.[Zheng-Xing], Song, M.[Mofei], Lang, X.F.[Xu-Feng],
Online 3D Shape Segmentation by Blended Learning,
MMMod15(I: 559-570).
Springer DOI 1501
3D shapes. BibRef

Tang, K.[Kevin], Joulin, A.[Armand], Li, L.J.[Li-Jia], Fei-Fei, L.[Li],
Co-localization in Real-World Images,
CVPR14(1464-1471)
IEEE DOI 1409
Co-localization; Object Detection of same class across images. BibRef

Hayder, Z.[Zeeshan], He, X.M.[Xu-Ming], Salzmann, M.[Mathieu],
Structural Kernel Learning for Large Scale Multiclass Object Co-detection,
ICCV15(2632-2640)
IEEE DOI 1602
BibRef
Earlier: A1, A3, A2:
Object Co-detection via Efficient Inference in a Fully-Connected CRF,
ECCV14(III: 330-345).
Springer DOI 1408
BibRef

Mairon, R.[Rotem], Ben-Shahar, O.[Ohad],
A Closer Look at Context: From Coxels to the Contextual Emergence of Object Saliency,
ECCV14(V: 708-724).
Springer DOI 1408
BibRef

Oramas Mogrovejo, J.A.[Jose Antonio], de Raedt, L.[Luc], Tuytelaars, T.[Tinne],
Towards cautious collective inference for object verification,
WACV14(269-276)
IEEE DOI 1406
Accuracy; Kernel relationships between objects to aid detection. BibRef

Anantharajah, K.[Kaneswaran], Ge, Z.Y.[Zong-Yuan], McCool, C.[Chris], Denman, S.[Simon], Fookes, C.[Clinton], Corke, P.[Peter], Tjondronegoro, D.[Dian], Sridharan, S.[Sridha],
Local inter-session variability modelling for object classification,
WACV14(309-316)
IEEE DOI 1406
Adaptation models BibRef

Misra, I.[Ishan], Shrivastava, A.[Abhinav], Hebert, M.[Martial],
Data-driven exemplar model selection,
WACV14(339-346)
IEEE DOI 1406
exemplars suitable for object detection. Detectors BibRef

Chen, X.[Xi], Jain, A.[Arpit], Davis, L.S.[Larry S.],
Object co-labeling in multiple images,
WACV14(721-728)
IEEE DOI 1406
jointly annotate multiple images of the same scene. Buildings BibRef

Song, Q.[Qi], Montillo, A.[Albert], Bhagalia, R.[Roshni], Srikrishnan, V.,
Organ Localization Using Joint AP/LAT View Landmark Consensus Detection and Hierarchical Active Appearance Models,
MCV13(138-147).
Springer DOI 1405
BibRef

Jiménez del Toro, Ó.A.[Óscar Alfonso], Müller, H.[Henning],
Hierarchic Multi-atlas Based Segmentation for Anatomical Structures: Evaluation in the VISCERAL Anatomy Benchmarks,
MCV14(189-200).
Springer DOI 1501
BibRef
Earlier:
Multi-structure Atlas-Based Segmentation Using Anatomical Regions of Interest,
MCV13(217-221).
Springer DOI 1405
BibRef

Joyseeree, R.[Ranveer], Jiménez del Toro, Ó.A.[Óscar Alfonso], Müller, H.[Henning],
Using Probability Maps for Multi-organ Automatic Segmentation,
MCV13(222-228).
Springer DOI 1405
BibRef

Xie, Y.R.[Yu-Rui], Huang, C.[Chao], Song, T.C.[Tie-Cheng], Ma, J.X.[Jin-Xiu], Jing, J.T.[Jie-Tao],
Object co-detection via low-rank and sparse representation dictionary learning,
VCIP13(1-6)
IEEE DOI 1402
image reconstruction BibRef

Guo, Q.J.[Qiao-Jin], Li, N.[Ning], Yang, Y.B.[Yu-Bin], Wu, G.S.[Gang-Shan],
Integrating image segmentation and annotation using supervised PLSA,
ICIP13(3800-3804)
IEEE DOI 1402
Image Annotation; Image Segmentation; PLSA BibRef

Cheng, J.Z.[Jie-Zhi], Chang, F.J.[Feng-Ju], Hsu, K.J.[Kuang-Jui], Lin, Y.Y.[Yen-Yu],
Knowledge Leverage from Contours to Bounding Boxes: A Concise Approach to Annotation,
ACCV12(I:730-744).
Springer DOI 1304
Class based segmentation BibRef

Jiang, Y.[Yu], Liu, J.[Jing], Li, Z.C.[Ze-Chao], Li, P.[Peng], Lu, H.Q.[Han-Qing],
Co-regularized PLSA for Multi-view Clustering,
ACCV12(II:202-213).
Springer DOI 1304
BibRef

Jiang, Y.[Yu], Liu, J.[Jing], Li, Z.C.[Ze-Chao], Lu, H.Q.[Han-Qing],
Collaborative PLSA for multi-view clustering,
ICPR12(2997-3000).
WWW Link. 1302
Clustering in one view should agree with other view BibRef

Ma, K.[Kai], Ben-Arie, J.[Jezekiel],
Multi-view multi-class object detection via exemplar compounding,
ICPR12(3256-3259).
WWW Link. 1302
BibRef

Yonetani, R.[Ryo], Kimura, A.[Akisato], Sakano, H.[Hitoshi], Fukuchi, K.[Ken],
Single Image Segmentation with Estimated Depth,
BMVC12(28).
DOI Link 1301
image plus depth BibRef

Mukherjee, L.[Lopamudra], Singh, V.[Vikas], Xu, J.[Jia], Collins, M.D.[Maxwell D.],
Analyzing the Subspace Structure of Related Images: Concurrent Segmentation of Image Sets,
ECCV12(IV: 128-142).
Springer DOI 1210
BibRef

Langmann, B.[Benjamin], Hartmann, K.[Klaus], Loffeld, O.[Otmar],
A Modular Framework for 2D/3D and Multi-modal Segmentation with Joint Super-Resolution,
CDC4CV12(II: 12-21).
Springer DOI 1210
BibRef

Stefanczyk, M.[Maciej], Kasprzak, W.[Wlodzimierz],
Multimodal Segmentation of Dense Depth Maps and Associated Color Information,
ICCVG12(626-632).
Springer DOI 1210
BibRef

Kasprzak, W.[Wlodzimierz], Stefanczyk, M.[Maciej],
3D Semantic Map Computation Based on Depth Map and Video Image,
ICCVG12(441-448).
Springer DOI 1210
BibRef

Akter, N.[Nasreen], Gondra, I.[Iker],
Attributed Relational Graph-Based Learning of Object Models for Object Segmentation,
ICIAR15(90-99).
Springer DOI 1507
BibRef

Gondra, I.[Iker], Alam, F.I.[Fahim Irfan],
Learning-Based Object Segmentation Using Regional Spatial Templates and Visual Features,
ICCVG12(397-406).
Springer DOI 1210
BibRef

Cushen, G.A.[George A.], Nixon, M.S.[Mark S.],
Real-time Semantic Clothing Segmentation,
ISVC12(I: 272-281).
Springer DOI 1209
BibRef

Collins, M.D.[Maxwell D.], Xu, J.[Jia], Grady, L.[Leo], Singh, V.[Vikas],
Random walks based multi-image segmentation: Quasiconvexity results and GPU-based solutions,
CVPR12(1656-1663).
IEEE DOI 1208
BibRef

Wang, H.Z.[Hong-Zhi], Yushkevich, P.A.[Paul A.],
Spatial bias in multi-atlas based segmentation,
CVPR12(909-916).
IEEE DOI 1208
BibRef

Rivera, P., Gould, S.,
Simultaneous Multi-class Pixel Labeling over Coherent Image Sets,
DICTA11(99-106).
IEEE DOI 1205
BibRef

Jayawardena, S., Yang, D.[Di], Hutter, M.,
3D Model Assisted Image Segmentation,
DICTA11(51-58).
IEEE DOI 1205
BibRef

Yang, Y.[Yi], Newsam, S.[Shawn],
Estimating the spatial extents of geospatial objects using hierarchical models,
WACV12(305-312).
IEEE DOI 1203
E.g. schools, golf courses usually marked only by a point. Automatically segment the extent. BibRef

Ibrahim, M.S.[Mostafa S.], El-Saban, M.[Motaz],
Higher order potentials with superpixel neighbourhood (HSN) for semantic image segmentation,
ICIP11(2881-2884).
IEEE DOI 1201
BibRef

Cao, Y.H.[Yi-Hui], Yuan, Y.[Yuan], Li, X.L.[Xue-Long], Yan, P.K.[Ping-Kun],
Putting images on a manifold for atlas-based image segmentation,
ICIP11(289-292).
IEEE DOI 1201
Medical analysis. Select similar shape in database. BibRef

Xu, W.T.[Wen-Tao], Kanawong, R.[Ratchadaporn], Duan, Y.[Ye], Zhang, G.X.[Gui-Xu],
A new information fusion approach for image segmentation,
ICIP11(2873-2876).
IEEE DOI 1201
BibRef

Eslami, S.M.A.[Seyed Mohammadali Ali], Williams, C.K.I.[Christopher K.I.],
Factored Shapes and Appearances for Parts-based Object Understanding,
BMVC11(xx-yy).
HTML Version. 1110
Some idea of the desired shape. BibRef

Wegner, J.D.[Jan D.], Rosenhahn, B.[Bodo], Sörgel, U.[Uwe],
Segmentation and Classification of Objects with Implicit Scene Context,
WTFCV11(264-284).
Springer DOI 1210
BibRef
And:
Implicit Scene Context for Object Segmentation and Classification,
DAGM11(31-40).
Springer DOI 1109
BibRef

Fu, H.[Hao], Qiu, G.P.[Guo-Ping],
Integrating Low-level and Semantic Features for Object Consistent Segmentation,
ICIG11(39-44).
IEEE DOI 1109
BibRef

Lu, C.Y.[Can-Yi], Feng, J.S.[Jia-Shi], Lin, Z.C.[Zhou-Chen], Yan, S.C.[Shui-Cheng],
Correlation Adaptive Subspace Segmentation by Trace Lasso,
ICCV13(1345-1352)
IEEE DOI 1403
BibRef

Liu, X.B.[Xiao-Bai], Feng, J.S.[Jia-Shi], Yan, S.C.[Shui-Cheng], Lin, L.[Liang], Jin, H.[Hai],
Segment an image by looking into an image corpus,
CVPR11(2249-2256).
IEEE DOI 1106
BibRef

Martins, C.I.O.[Charles Iury Oliveira], Cesar, R.M.[Roberto Marcondes], Jorge, L.R.[Leonardo Ré], Freitas, A.V.L.[André Victor Lucci],
Segmentation of Similar Images Using Graph Matching and Community Detection,
GbRPR11(265-274).
Springer DOI 1105
Interactive segmentation of one, leads to automatic segmentation of rest in dataset. Biological samples (butterfly wings). BibRef

Cusano, C.[Claudio],
Region-Based Annotation of Digital Photographs,
CCIW11(47-59).
Springer DOI 1104
Oversegment, classify into 7 catetories, discard low confidence. BibRef

Aue, A.[Alexander], Lee, T.C.M.[Thomas C. M.],
Statistically consistent image segmentation,
ICIP10(2229-2232).
IEEE DOI 1009
BibRef

Yang, W.[Wen], Dai, D.X.[Deng-Xin], Triggs, B.[Bill], Xia, G.S.[Gui-Song], He, C.[Chu],
Fast semantic scene segmentation with conditional random field,
ICIP10(229-232).
IEEE DOI 1009
BibRef

Wu, S.L.[Shi-Lin], Geng, J.J.[Jia-Jia], Zhu, F.[Feng],
Theme-Based Multi-class Object Recognition and Segmentation,
ICPR10(3013-3016).
IEEE DOI 1008
assign a theme BibRef

Walter, M.R.[Matthew R.], Friedman, Y.[Yuli], Antone, M.[Matthew], Teller, S.[Seth],
Appearance-based object reacquisition for mobile manipulation,
CVforHRI10(1-8).
IEEE DOI 1006
Interactively give task information, use this for other viewpoints. BibRef

Ghosh, S.[Soumya], Pfeiffer, J.J.[Joseph J.], Mulligan, J.[Jane],
A general framework for reconciling multiple weak segmentations of an image,
WACV09(1-8).
IEEE DOI 0912
BibRef

Moore, A.P.[Alastair P.], Prince, S.J.D.[Simon J.D.], Warrell, J.[Jonathan],
'Lattice Cut': Constructing superpixels using layer constraints,
CVPR10(2117-2124).
IEEE DOI 1006
BibRef

Warrell, J.[Jonathan], Prince, S.J.D.[Simon J.D.], Moore, A.P.[Alastair P.],
Epitomized priors for multi-labeling problems,
CVPR09(2812-2819).
IEEE DOI 0906
Combine local and context for segmentation. BibRef

Hochbaum, D.S.[Dorit S.],
An Efficient and Effective Tool for Image Segmentation, Total Variations and Regularization,
SSVM11(338-349).
Springer DOI 1201
BibRef

Driesen, J., Scheunders, P.,
A Multicomponent Image Segmentation Framework,
ACIVS08(xx-yy).
Springer DOI 0810
BibRef

Xiang, B.[Bo], Komodakis, N.[Nikos], Paragios, N.[Nikos],
Pose Invariant Deformable Shape Priors Using L_1 Higher Order Sparse Graphs,
ISVC13(I:194-205).
Springer DOI 1310
Knowledge based segmentation. BibRef

Besbes, A.[Ahmed], Komodakis, N.[Nikos], Langs, G.[Georg], Paragios, N.[Nikos],
Shape priors and discrete MRFs for knowledge-based segmentation,
CVPR09(1295-1302).
IEEE DOI 0906
BibRef

Essafi, S.[Salma], Langs, G.[Georg], Paragios, N.[Nikos],
Hierarchical 3D diffusion wavelet shape priors,
ICCV09(1717-1724).
IEEE DOI 0909
Knowledge for segmentation. Snakes? BibRef

Essafi, S.[Salma], Langs, G.[Georg], Paragios, N.[Nikos],
Sparsity, redundancy and optimal image support towards knowledge-based segmentation,
CVPR08(1-7).
IEEE DOI 0806
BibRef

Ahmad, J.E.[Jawad Elsayed], Takakura, Y.[Yoshitate],
Improving Segmentation Maps using Polarization Imaging,
ICIP07(I: 281-284).
IEEE DOI 0709
BibRef

Wallhoff, F.[Frank], Rub, M.[Martin], Rigoll, G.[Gerhard], Gobel, J.[Johann], Diehl, H.[Hermann],
Improved Image Segmentation using Photonic Mixer Devices,
ICIP07(VI: 53-56).
IEEE DOI 0709
BibRef

Gallagher, C.[Claire], Kokaram, A.[Anil],
Bayesian Example Based Segmentation using a Hybrid Energy Model,
ICIP07(II: 41-44).
IEEE DOI 0709
BibRef

Hong, X.[Xin], McClean, S.[Sally], Scotney, B.W.[Bryan W.], Morrow, P.J.[Philip J.],
Model-Based Segmentation of Multimodal Images,
CAIP07(604-611).
Springer DOI 0708
BibRef

Vasconcelos, M.[Manuela], Vasconcelos, N.M.[Nuno M.], Carneiro, G.[Gustavo],
Weakly Supervised Top-down Image Segmentation,
CVPR06(I: 1001-1006).
IEEE DOI 0606
BibRef

Borenstein, E.[Eran], Malik, J.[Jitendra],
Shape Guided Object Segmentation,
CVPR06(I: 969-976).
IEEE DOI 0606
BibRef

Johnson, M.A., Cipolla, R.,
Improved Image Annotation and Labelling through Multi-Label Boosting,
BMVC05(xx-yy).
HTML Version. 0509
Learning based on multi-labelled data. BibRef

Clark, A.A., Thomas, B.T.,
Evolving Image Segmentations for the Analysis of Video Sequences,
CVPR01(II:290-295).
IEEE DOI 0110
Use initial segmentation to guide later ones. BibRef

Perner, P.[Petra],
Controlling the Segmentation Parameters by Case-based Reasoning,
ICPR00(Vol III: 963-966).
IEEE DOI 0009
BibRef
Earlier:
Case based reasoning for image interpretation,
CAIP95(532-537).
Springer DOI 9509
BibRef

Hug, J., Brechbuhler, C., Szekely, G.,
Model-Based Initialisation for Segmentation,
ECCV00(II: 290-306).
Springer DOI 0003
BibRef

Charroux, B., Philipp, S., Cocquerez, J.P.,
Image Analysis: Segmentation Operator Cooperation Led by the Interpretation,
ICIP96(III: 939-942).
IEEE DOI BibRef 9600

Paulus, D.,
Object oriented image segmentation,
ICIPA92(482-485).
PS File. BibRef 9200

Baker, D.C., Aggarwal, J.K., and Hwang, V.S.,
Geometry Guided Incremental Segmentation,
CVWS87(237-239). BibRef 8700

Price, K.E., Medioni, G.,
Segmentation Using Scene Models,
USC_ISG-102, October 1982. BibRef 8210 USC Computer VisionCombine region and edge models. BibRef

Medioni, G.G.[Gerard G.],
Segmentation of Images Into Regions Using Edge Information,
AAAI-82(42-45). BibRef 8200 USC Computer Vision BibRef

Sze, T.W., and Yang, Y.H.,
Goal Directed Segmentation,
PRIP82(504-511). How to use some of the a priori information in segmentation (such as shape). Compares this to gradient based boundary extraction followed by labeling of inside or outside (unclean). Also to split and merge. Method generates initial regions (either method) polygonal approximation, shape matching using syntactic methods and merge regions which improve the shape match. BibRef 8200

Aggarwal, R.K.,
Adaptive Image Segmentation Using Prototype Similarity,
PRIP78(354-359) BibRef 7800

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