Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event n-Grams
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Grouping with Asymmetric Affinities: A Game-Theoretic Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Dominant Sets and Pairwise Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-Based Hierarchical Image Matching
International Journal of Computer Vision
Fast communication: Dominant sets clustering for image retrieval
Signal Processing
Graph-based quadratic optimization: A fast evolutionary approach
Computer Vision and Image Understanding
Multiple-instance learning with instance selection via dominant sets
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
| Hi-index | 0.00 |
We propose a fast population game dynamics, motivated by the analogy with infection and immunization processes within a population of "players," for finding dominant sets, a powerful graph-theoretical notion of a cluster. Each step of the proposed dynamics is shown to have a linear time/space complexity and we show that, under the assumption of symmetric affinities, the average population payoff is strictly increasing along any non-constant trajectory, thereby allowing us to prove that dominant sets are asymptotically stable (i.e., attractive) points for the proposed dynamics. The approach is general and can be applied to a large class of quadratic optimization problems arising in computer vision. Experimentally, the proposed dynamics is found to be orders of magnitude faster than and as accurate as standard algorithms.