JACIII Vol.14 No.4 pp. 375-381
doi: 10.20965/jaciii.2010.p0375


Color Quantization Based on Hierarchical Frequency Sensitive Competitive Learning

Jun Zhang and Jinglu Hu

Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan

August 31, 2009
February 26, 2010
May 20, 2010
color quantization (CQ), tree structure, splitmerging conditions

In this paper, we propose a Hierarchical Frequency Sensitive Competitive Learning (HFSCL) method to achieve Color Quantization (CQ). In HFSCL, the appropriate number of quantized colors and the palette can be obtained by an adaptive procedure following a binary tree structure with nodes and layers. Starting from the root node that contains all colors in an image until all nodes are examined by split conditions, a binary tree will be generated. In each node of the tree, a Frequency Sensitive Competitive Learning (FSCL) network is used to achieve two-way division. To avoid over-split, merging condition is defined to merge the clusters that are close enough to each other at each layer. Experimental results show that the proposed HFSCL has desired ability for CQ.

Cite this article as:
Jun Zhang and Jinglu Hu, “Color Quantization Based on Hierarchical Frequency Sensitive Competitive Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.14, No.4, pp. 375-381, 2010.
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