Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete Applied Mathematics
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Object Categorization by Learned Universal Visual Dictionary
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
Polynomial Time Algorithms for Ratio Regions and a Variant of Normalized Cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-segmentation of image pairs with quadratic global constraint in MRFs
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Cosegmentation revisited: models and optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
MOMI-cosegmentation: simultaneous segmentation of multiple objects among multiple images
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Contour-based joint clustering of multiple segmentations
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A closed form solution to robust subspace estimation and clustering
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Scale invariant cosegmentation for image groups
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Hi-index | 0.00 |
We develop new algorithms to analyze and exploit the joint subspace structure of a set of related images to facilitate the process of concurrent segmentation of a large set of images. Most existing approaches for this problem are either limited to extracting a single similar object across the given image set or do not scale well to a large number of images containing multiple objects varying at different scales. One of the goals of this paper is to show that various desirable properties of such an algorithm (ability to handle multiple images with multiple objects showing arbitary scale variations) can be cast elegantly using simple constructs from linear algebra: this significantly extends the operating range of such methods. While intuitive, this formulation leads to a hard optimization problem where one must perform the image segmentation task together with appropriate constraints which enforce desired algebraic regularity (e.g., common subspace structure). We propose efficient iterative algorithms (with small computational requirements) whose key steps reduce to objective functions solvable by max-flow and/or nearly closed form identities. We study the qualitative, theoretical, and empirical properties of the method, and present results on benchmark datasets.