An adaptive clustering algorithm for color quantization

Ing Sheen Hsieh, Kuo Chin Fan

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

In this paper, a novel adaptive clustering algorithm for color image quantization is presented. In our approach, a superposed 3D histogram is calculated first. Then, the sorted histogram list is fed into an adaptive clustering algorithm to extract the palette colors in the image. Finally, a destined pixel-mapping algorithm is applied to classify pixels into their corresponding palette colors. The quantized error of our proposed algorithm is very small due to the combination of the reduced RGB color space utilization and the adaptive clustering algorithm. Besides, the executing speed of our proposed algorithm is also quite fast due to the reduced RGB color space, sorted histogram list, suitable color design and destined pixel mapping. Experimental results reveal the feasibility and superiority of our proposed approach in solving color quantization problem.

Original languageEnglish
Pages (from-to)337-346
Number of pages10
JournalPattern Recognition Letters
Volume21
Issue number4
DOIs
StatePublished - Apr 2000

Keywords

  • Adaptive clustering
  • Color quantization
  • Destined pixel mapping
  • Superposed histogram

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