A comparison of stochastic optimization techniques for image segmentation
Suchendra M. Bhandarkar, Hui Gary Zhang · International Journal of Intelligent Systems · 2000
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 optimization techniques, namely, simulated annealing (SA), microcanonical annealing (MCA), 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. © 2000 John Wiley & Sons, Inc.