Discrete Applied Mathematics
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
Seed image selection in interactive cosegmentation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
Interactively Co-segmentating Topically Related Images with Intelligent Scribble Guidance
International Journal of Computer Vision
Interactive segmentation with super-labels
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Multiple-instance learning with structured bag models
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
A bag-of-objects retrieval model for web image search
Proceedings of the 20th ACM international conference on Multimedia
Proceedings of the 20th ACM international conference on Multimedia
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
Joint co-segmentation and registration of 3D ultrasound images
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Learning discriminative localization from weakly labeled data
Pattern Recognition
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The problem of cosegmentation consists of segmenting the same object (or objects of the same class) in two or more distinct images. Recently a number of different models have been proposed for this problem. However, no comparison of such models and corresponding optimization techniques has been done so far. We analyze three existing models: the L1 norm model of Rother et al. [1], the L2 norm model of Mukherjee et al. [2] and the "reward" model of Hochbaum and Singh [3]. We also study a new model, which is a straightforward extension of the Boykov-Jolly model for single image segmentation [4]. In terms of optimization, we use a Dual Decomposition (DD) technique in addition to optimization methods in [1,2]. Experiments show a significant improvement of DD over published methods. Our main conclusion, however, is that the new model is the best overall because it: (i) has fewest parameters; (ii) is most robust in practice, and (iii) can be optimized well with an efficient EM-style procedure.