Abstract
This paper proposes a hybrid of differential evolution and genetic algorithms to solve the color image segmentation problem. Clustering based color image segmentation algorithms segment an image by clustering the features of color and texture, thereby obtaining accurate prototype cluster centers. In the proposed algorithm, the color features are obtained using the homogeneity model. A new texture feature named Power Law Descriptor (PLD) which is a modification of Weber Local Descriptor (WLD) is proposed and further used as a texture feature for clustering. Genetic algorithms are competent in handling binary variables, while differential evolution on the other hand is more efficient in handling real parameters. The obtained texture feature is binary in nature and the color feature is a real value, which suits very well the hybrid cluster center optimization problem in image segmentation. Thus in the proposed algorithm, the optimum texture feature centers are evolved using genetic algorithms, whereas the optimum color feature centers are evolved using differential evolution.
Highlights
Color image segmentation is to divide a chromatic image into different homogeneous and connected regions based on color, texture and their combination [10]
This basic algorithm was evolved to the Rough k-means (RKM) that was proposed in [12] borrowing some of the concepts of rough set theory [19] and rough fuzzy c-means algorithm which was applied to medical image segmentation problem [15]
The fundamental steps of Soft Rough Fuzzy-c-Means (SRFCM) are as follows: (1) assume m random initial cluster prototypes (2) find membership uik between m cluster centers and k data points (3) allocate each data point to the lower or upper approximation and (4) make the final assignment based on the difference between the highest and highest membership of a data point in all clusters (5) compute the similarity of sample points soft set to the cluster centre soft set, calculate the maximum similarity and assign a pixel to a cluster to which it has maximum similarity after fuzzification (6) compute the updated cluster prototype for each cluster (7) iterate and run steps 2–6 until there are no further changes in cluster centroids
Summary
Color image segmentation is to divide a chromatic image into different homogeneous and connected regions based on color, texture and their combination [10]. The consistency of clustering based segmentation methods such as k-means, Fuzzy-c-Means, etc are limited by the initially chosen cluster centers, and on the cardinality of chosen cluster centers. This problem is solved by using evolutionary computing in this paper. An Improved differential evolution method has been proposed in to optimize the multi-objective parameters in fuzzy clustering [14] as well as GAs [4]. GA operates on the texture part and DE operates on the color part, so that the hybrid optimizer effectively explores both the binary and real search domain
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