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Proceedings Paper

Game theoretic approach to similarity-based image segmentation
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Paper Abstract

Image segmentation decomposes a given image into segments, i.e. regions containing "similar" pixels, that aids computer vision applications such as face, medical, and fingerprint recognition as well as scene characterization. Effective segmentation requires domain knowledge or strategies for object designation as no universal segmentation algorithm exists. In this paper, we propose a similarity based image segmentation approach based on game theory methods. The essential idea behind our approach is that the similarity based clustering problem can be considered as a "clustering game". Within this context, the notion of a cluster turns out to be equivalent to a classical equilibrium concept from game theory, as the game equilibrium reflects both the internal and external cluster conditions. We also show that there exists a correspondence between these equilibriums and the local solutions of a polynomial, linearlyconstrained, optimization problem, and provide an algorithm for finding the equalibirums. Experiments on image segmentation problems show the superiority of the proposed clustering game image segmentation (CGIS) approach using a common data set of visual images in autonomy, speed, and efficiency.

Paper Details

Date Published: 17 September 2011
PDF: 12 pages
Proc. SPIE 8137, Signal and Data Processing of Small Targets 2011, 813708 (17 September 2011); doi: 10.1117/12.896681
Show Author Affiliations
Dan Shen, Independent Consultant (United States)
Genshe Chen, Independent Consultant (United States)
Yufeng Zheng, Alcorn State Univ. (United States)
Erik Blasch, Air Force Research Lab. (United States)
Khanh Pham, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 8137:
Signal and Data Processing of Small Targets 2011
Oliver E. Drummond, Editor(s)

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