Visual reconstruction
Algorithms for clustering data
Algorithms for clustering data
Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
A randomized algorithm for pairwise clustering
Proceedings of the 1998 conference on Advances in neural information processing systems II
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative Analysis of Grouping Processes
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
A Factorization Approach to Grouping
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
K-Means with Large and Noisy Constraint Sets
ECML '07 Proceedings of the 18th European conference on Machine Learning
A discriminative learning framework with pairwise constraints for video object classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning spatio-temporal dependency of local patches for complex motion segmentation
Computer Vision and Image Understanding
Multi-view classification with cross-view must-link and cannot-link side information
Knowledge-Based Systems
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Grouping is a global partitioning process that integrates local cues distributed over the entire image. We identify four types of pairwise relationships, attraction and repulsion, each of which can be symmetric or asymmetric. We represent these relationships with two directed graphs. We generalize the normalized cuts criteria to partitioning on directed graphs. Our formulation results in Rayleigh quotients on Hermitian matrices, where the real part describes undirected relationships, with positive numbers for attraction, negative numbers for repulsion, and the imaginary part describes directed relationships. Globally optimal solutions can be obtained by eigendecomposition. The eigenvectors characterize the optimal partitioning in the complex phase plane, with phase angle separation determining the partitioning of vertices and the relative phase advance indicating the ordering of partitions. We use directed repulsion relationships to encode relative depth cues and demonstrate that our method leads to simultaneous image segmentation and depth segregation.