What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Boosting Saliency in Color Image Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Bi-Layer Segmentation of Binocular Stereo Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Iterative Optimization Approach for Unified Image Segmentation and Matting
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Interactively Co-segmentating Topically Related Images with Intelligent Scribble Guidance
International Journal of Computer Vision
Analyzing the subspace structure of related images: concurrent segmentation of image sets
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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This paper provides a novel method for co-segmentation, namely simultaneously segmenting multiple images with same foreground and distinct backgrounds. Our contribution primarily lies in four-folds. First, image pairs are typically captured under different imaging conditions, which makes the color distribution of desired object shift greatly, hence it brings challenges to color-based co-segmentation. Here we propose a robust regression method to minimize color variances between corresponding image regions. Secondly, although having been intensively discussed, the exact meaning of the term "co-segmentation" is rather vague and importance of image background is previously neglected, this motivate us to provide a novel, clear and comprehensive definition for co-segmentation. Thirdly, it is an involved issue that specific regions tend to be categorized as foreground, so we introduce "risk term" to differentiate colors, which has not been discussed before in the literatures to our best knowledge. Lastly and most importantly, unlike conventional linear global terms in MRFs, we propose a sum-of-squared-difference (SSD) based global constraint and deduce its equivalent quadratic form which takes into account the pairwise relations in feature space. Reasonable assumptions are made and global optimal could be efficiently obtained via alternating Graph Cuts.