Color image segmentation: a novel spatial fuzzy genetic algorithm

Abstract

Image segmentation is a very important low-level vision task. It is the perceptual grouping of pixels based on some similarity criteria. In this article, we have applied spatial fuzzy genetic algorithm (SFGA) for the unsupervised segmentation of color images. The SFGA adds diversity to the search process to find the global optima. The performance of SFGA is influenced by two factors: first, K number of clusters—should be known in advance; second, the initialization of the cluster centers. To overcome these issues, a progressive technique based on self-organizing map is presented to find out the optimal K number of clusters automatically. To handle the initialization problem, peaks are identified using the image color histograms. The genetic algorithm with fuzzy behavior maximizes the fuzzy separation and minimizes the global compactness among the segments. The segmentation is performed on wavelet transform image which not only reduces the dimensionality and computational cost but also makes more compact segments. A novel pruning technique is proposed to handle the problem of over-segmentation. The results show that the proposed technique outperforms state-of-the-art methods.