Volume 5 Number 2 (Feb. 2010)
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JCP 2010 Vol.5(2): 202-209 ISSN: 1796-203X
doi: 10.4304/jcp.5.2.202-209

Adaptive Extraction of Principal Colors Using an Improved Self-Growing Network

Yurong Li1, 2, Zhengdong Du3, and Hongguang Fu2
1 School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 610074, China
2 Chengdu Institute of Computer Application, the Chinese Academy of Science, Chengdu 610041, China
3 Department of Mathematics, Sichuan University, Chengdu, Sichuan 610064, China


Abstract—This paper aims to solve the two major issues existing in current color quantization algorithms. The first one is to require users to specify the number of representative colors in advance; the other is that it is difficult in choosing the colors to describe accurately the essential details represented by small groups of pixels isolated in the color space. Based on the growing mechanism of the Growing When Required neural network, a novel algorithm is proposed to adaptively extract the prominent colors of an image. A number of criteria are introduced that have an effect on controlling of the number and topology of neurons in the output layer. A global permutation method to rearrange the input sample order is presented based on Linear Pixels Shuffling in order to improve the performance of the network. The experiments show that the proposed method can automatically estimate the number of colors to efficiently represent an original image, meanwhile capable of retaining important isolated colors even when the number of the representative colors is low. It is also shown that the algorithm outperforms the popular ones in terms of color distortion.

Index Terms—color quantization, incremental learning, self-growing network, neural network, Linear Pixel Shuffling

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Cite: Yurong Li, Zhengdong Du, and Hongguang Fu, " Adaptive Extraction of Principal Colors Using an Improved Self-Growing Network," Journal of Computers vol. 5, no. 2, pp. 202-209, 2010.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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