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.