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Color Image Quantization Algorithm Based on Self-Adaptive Differential Evolution

机译:基于自适应差分进化的彩色图像量化算法

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摘要

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, show that the proposed algorithm is a practicable quantization method and is more competitive than K-means and particle swarm algorithm (PSO) for the color image quantization.
机译:差分进化算法(DE)是一种新颖的随机优化方法。它在彩色图像量化问题上具有较好的性能,但是很难为用户设置DE的参数。提出了一种基于自适应DE的彩色图像量化算法。该算法采用自适应机制在进化过程中自动调整DE的参数,并采用DE和K-means混合机制来增强局部搜索。在一组常用测试图像上的数值实验结果表明,该算法是一种可行的量化方法,并且在彩色图像量化方面比K-means和粒子群算法(PSO)更具竞争力。

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