A game-theoretical approach to image segmentation

Authors:
Jing Li;Gang Zeng;Rui Gan;Hongbin Zha;Long Wang
Affiliations:
Key Laboratory of Machine Perception, Peking University, Beijing, China;Key Laboratory of Machine Perception, Peking University, Beijing, China;Key Laboratory of Machine Perception, Peking University, Beijing, China;Key Laboratory of Machine Perception, Peking University, Beijing, China;Key Laboratory of Machine Perception, Peking University, Beijing, China
Venue:
CVM'12 Proceedings of the First international conference on Computational Visual Media
Year:
2012

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Abstract

This paper describes a novel algorithm for image segmentation within the framework of evolutionary game theory. Beyond the pairwise model, our objective function enables exploration on larger patches by introducing clique probability, and enforcing pixels within clique be assigned the same label. By combining the Public Goods Game, our algorithm can efficiently solve the multi-label segmentation problem. Experiments on challenging datasets demonstrate that our algorithm outperforms the state-of-art. We believe that this algorithm can be extended to many other labeling problems.