Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation

Authors

  • R. V. V. Krishna Aditya College of Engineering & Technology, Kakinada, India
  • S. Srinivas Kumar Department of ECE, University College of Engineering, Jawaharlal Nehru Technological University Kakinada (JNTUK), Andhra Pradesh, India
Volume: 6 | Issue: 5 | Pages: 1182-1186 | October 2016 | https://doi.org/10.48084/etasr.799

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.

Keywords:

differential evolution, genetic algorithm, clustering, segmentation, hybrid algorithms, rough sets, fuzzy sets, soft sets

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How to Cite

[1]
R. V. V. Krishna and S. Srinivas Kumar, “Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation”, Eng. Technol. Appl. Sci. Res., vol. 6, no. 5, pp. 1182–1186, Oct. 2016.

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