A reinforcement-learning approach to color quantization

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

A reinforcement-learning approach to color quantization

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

資訊工程學系

研究成果: 雜誌貢獻 › 會議論文 › 同行評審

摘要

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.

原文	???core.languages.en_GB???
頁（從 - 到）	94-99
頁數	6
期刊	Proceedings of the IASTED International Conference on Intelligent Systems and Control
出版狀態	已出版 - 2004
事件	Proceedings of the Sixth IASTED International Conference on Intelligent Systems and Control - Honolulu, HI, United States 持續時間: 23 8月 2004 → 25 8月 2004

引用此

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title = "A reinforcement-learning approach to color quantization",

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.",

keywords = "Classifier systems, Color quantization, Neuro-fuzzy systems, Reinforcement learning",

author = "Chou, {Chien Hsing} and Su, {Mu Chun} and Fu Chang and Eugene Lai",

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journal = "Proceedings of the IASTED International Conference on Intelligent Systems and Control",

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note = "Proceedings of the Sixth IASTED International Conference on Intelligent Systems and Control ; Conference date: 23-08-2004 Through 25-08-2004",

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TY - JOUR

T1 - A reinforcement-learning approach to color quantization

AU - Chou, Chien Hsing

AU - Su, Mu Chun

AU - Chang, Fu

AU - Lai, Eugene

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

KW - Classifier systems

KW - Color quantization

KW - Neuro-fuzzy systems

KW - Reinforcement learning

UR - http://www.scopus.com/inward/record.url?scp=10444241140&partnerID=8YFLogxK

M3 - 會議論文

AN - SCOPUS:10444241140

SN - 1025-8973

SP - 94

EP - 99

JO - Proceedings of the IASTED International Conference on Intelligent Systems and Control

JF - Proceedings of the IASTED International Conference on Intelligent Systems and Control

T2 - Proceedings of the Sixth IASTED International Conference on Intelligent Systems and Control

Y2 - 23 August 2004 through 25 August 2004

ER -

A reinforcement-learning approach to color quantization

摘要

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引用此