A hierarchical clustering method for color quantization

Jun Zhang, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 HFSCL has the desired ability for CQ.

Original languageEnglish
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages786-789
Number of pages4
DOIs
Publication statusPublished - 2010 Nov 18
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: 2010 Aug 232010 Aug 26

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period10/8/2310/8/26

Keywords

  • Color quantization(CQ)
  • Competitive learning
  • Tree structure

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Zhang, J., & Hu, J. (2010). A hierarchical clustering method for color quantization. In Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 (pp. 786-789). [5596046] (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2010.198