Presentation	2014-12-18 Color Quantization Using Growing Self-Organizing Map Kazuhiro TOKUNAGA, Noriaki SUETAKE,
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Abstract(in English)	This work aims to verify a performance of a color quantization using growing self-organizing map. Authors have proposed a stochastically-growing self-organizing map based on a learning algorithm of an evolving self-organizing map (ESOM) proposed by Deng. The proposed method has a structure of graph network in which a reference vector unit and a graph-path stochastically grow in self-organizing manner. In this work, the proposed method obtained the smallest quantization error comparing with other quantization methods. Moreover, it was possible to obtain a sufficient quantization performance with less training samples. Furthermore, in the color quantization using the growing self-organizing map, it was possible to generate a pallet for the less frequency of colors in the image.
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Keyword(in English)	Growing Self-Organizing Map / Color Quantization
Paper #	SIS2014-74
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Committee	SIS
Conference Date	2014/12/11(1days)
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Registration To	Smart Info-Media Systems (SIS)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Color Quantization Using Growing Self-Organizing Map
Sub Title (in English)
Keyword(1)	Growing Self-Organizing Map
Keyword(2)	Color Quantization
1st Author's Name	Kazuhiro TOKUNAGA
1st Author's Affiliation	National Fisheries University()
2nd Author's Name	Noriaki SUETAKE
2nd Author's Affiliation	Graduate School of Science and Engineering, Yamaguchi University
Date	2014-12-18
Paper #	SIS2014-74
Volume (vol)	vol.114
Number (no)	370
Page	pp.pp.-
#Pages	6
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