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Robust image segmentation using genetic algorithm with a fuzzy measure
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DOI：10.1016/0031-3203(95)00148-4.png)
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Abstract
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In this paper we present new region-based image segmentation methodology on gray-level images using a genetic algorithm with a fuzzy measure. We first propose a fuzzy validity function which measures a degree of separation and compactness between and within finely segmented regions, and an edge strength along boundaries of all regions. We apply the generic algorithm to search a good or usable region segmentation, which maximizes the quality of regions generated by split- and-merge processing. The iterative algorithm provides a useful method for image segmentation without the need for critical parameters or threshold values, iterative visual interaction or a priori knowledge of an image. Copyright (C) 1996 Pattern Recognition Society.
Keywords:
genetic algorithm
split-and-merge image segmentation
validity measurement
fuzzy objective function
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