English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 55241/89544 (62%)
造訪人次 : 10729832      線上人數 : 22
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/55826


    題名: A Reinforcement-Learning Approach to Color Quantization
    作者: Chou, Chien-Hsing;Su, Mu-Chun;Zhao, Yu-Xiang;Hsu, Fu-Hau
    貢獻者: 淡江大學電機工程學系
    關鍵詞: Color Quantization;Color Reduction;Classifier Systems;Pattern Recognition;Reinforcement Learning;Neuro-Fuzzy Systems;Machine Learning
    日期: 2011-06-01
    上傳時間: 2011-08-28 16:43:22 (UTC+8)
    出版者: 新北市:淡江大學
    摘要: Color quantization is a process of sampling three-dimensional color space (e.g. RGB) to reduce the number of colors in a color image. By reducing to a discrete subset of colors known as a color codebook or palette, each pixel in the original image is mapped to an entry according to these palette colors. In this paper, a reinforcement-learning approach to color image quantization is proposed. Fuzzy rules, which are used to select appropriate parameters for the adaptive clustering algorithm applied to color quantization, are built through reinforcement learning. By comparing this new method with the original adaptive clustering algorithm on 30 color images, our method shows an improvement of 3.3% to 5.8% in peak signal to noise ratio (PSNR) values on average and results in savings of about 10% in computation time. Moreover, we demonstrate that reinforcement learning is an efficacious as well as efficient way to provide a solution of the learning problem where there is a lack of knowledge regarding the input-output relationship.
    關聯: Tamkang Journal of Science and Engineering=淡江理工學刊 14(2), pp.141-150
    DOI: 10.6180/jase.2011.14.2.07
    顯示於類別:[電機工程學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    A Reinforcement-Learning Approach to Color Quantization.pdf4592KbAdobe PDF492檢視/開啟
    index.html0KbHTML156檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋