A reinforcement-learning approach to color quantization

Chien Hsing Chou, Mu Chun Su, Fu Chang, Eugene Lai

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper, a reinforcement-learning approach is proposed to color image quantization. Fuzzy rules, which select appropriate parameters for the adaptive clustering algorithm applied to color quantization, are built through the reinforcement learning. Experiment examples are tested to demonstrate the performance of the proposed system.

Original languageEnglish
Pages (from-to)94-99
Number of pages6
JournalProceedings of the IASTED International Conference on Intelligent Systems and Control
StatePublished - 2004
EventProceedings of the Sixth IASTED International Conference on Intelligent Systems and Control - Honolulu, HI, United States
Duration: 23 Aug 200425 Aug 2004

Keywords

  • Classifier systems
  • Color quantization
  • Neuro-fuzzy systems
  • Reinforcement learning

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