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A comparison of stochastic optimization techniques for image segmentation

✍ Scribed by Suchendra M. Bhandarkar; Hui Zhang

Publisher
John Wiley and Sons
Year
2000
Tongue
English
Weight
871 KB
Volume
15
Category
Article
ISSN
0884-8173

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✦ Synopsis

Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogenous. A segmented image is considered to be the highest domain-independent abstraction of an input image. In this paper, the image segmentation problem is treated as one of combinatorial optimization. A cost function which incorporates both, edge information and region gray-scale variances is defined. The cost function is shown to be multivariate with several local minima. Three stochastic optimiza-Ž . Ž . tion techniques, namely, simulated annealing SA , microcanonical annealing MCA , Ž . and the random cost algorithm RCA are investigated and compared in the context of minimization of the aforementioned cost function for image segmentation. Experimental results on gray-scale images are presented.

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