Color image quantization algorithm based on self-adaptive differential evolution

Authors:
Qinghua Su;Zhongbo Hu
Affiliations:
School of Mathematics and Statistic, Hubei Engineering University, Xiaogan, Hubei, China;School of Mathematics and Statistic, Hubei Engineering University, Xiaogan and School of Sciences, Wuhan University of Technology, Wuhan, Hubei, China
Venue:
Computational Intelligence and Neuroscience
Year:
2013

Citing 4
Cited 0

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Performance comparison of self-adaptive and adaptive differential evolution algorithms

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Automatic image pixel clustering with an improved differential evolution

Applied Soft Computing
Comparison and optimization of methods of color image quantization

IEEE Transactions on Image Processing

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Abstract

Differential evolution algorithm (DE) is one of the novel stochastic optimization methods. It has a better performance in the problem of the color image quantization, but it is difficult to set the parameters of DE for users. This paper proposes a color image quantization algorithm based on self-adaptive DE. In the proposed algorithm, a self-adaptive mechanic is used to automatically adjust the parameters of DE during the evolution, and a mixed mechanic of DE and K-means is applied to strengthen the local search. The numerical experimental results, on a set of commonly used test images, showthat the proposed algorithmis a practicable quantization method and is more competitive than K-means and particle swarm algorithm (PSO) for the color image quantization.