Two modified crossover and mutation operators for image segmentation by genetic algorithms

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Abstract
Segmentation of an image refers to partitioning the image into several subimages such that each subimage forms a connected component representing a logical entity present in the scene, and all the segments as a whole produce a meaningful interpretation of the scene being studied. The problem is inherently of NP nature and it is as hard as the simplest possible NP- complete problem called partition. Existence of the unique solution (the truly optimal segmentation) and its sensitivity to the sampling process is yet to be studied thoroughly. The formulation and implementation of a randomized search approach to segment an image, using genetic algorithms, is presented in this paper. A state space representation of partially segmented image using binary strings is considered. The dominant substrings are easily explained in terms of chromosomes. Also the operations such as crossover and mutations are easily abstracted. A modified crossover operator using boundary interaction and region adjacency graph (BIRAG) has been adopted to improve the performance. Also, a simplified mutation operator called `switch' has been devised using the BIRAG. In particular, the specific data structure used in this scheme also facilitates a means for accommodating pixel- level feed back, and model based bias for model based segmentation of images. Images from two different scenes are segmented using this approach to illustrate the applicability of such a system.

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