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| 論文名稱 | Markov chain model based on genetic algorithms for texture and speech recognition |
| 發表日期 | 2006-12-01 |
| 論文收錄分類 | SCI |
| 所有作者 | Shiueng Bien Yang |
| 作者順序 | 第一作者 |
| 通訊作者 | 否 |
| 刊物名稱 | Journal of Electronic Imaging |
| 發表卷數 | 15 |
| 是否具有審稿制度 | 是 |
| 發表期數 | 3 |
| 期刊或學報出版地國別/地區 | NATTWN-中華民國 |
| 發表年份 | 2006 |
| 發表月份 | 12 |
| 發表形式 | 電子期刊 |
| 所屬計劃案 | 無 |
| 可公開文檔 |
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| 可公開文檔 |
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| 可公開文檔 | |
| 附件 |
 Markov chain model based on genetic algorithms for texture and speech recognition .pdf
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[英文摘要] :
Markov chain models (MCMs) were recently adopted in
many recognition applications. The well-known clustering algorithm,
the k-means algorithm, is used to design the codebooks of the
MCM, and then each code word in the codebook is regarded as one
state of MCM. However, users usually have no idea how to determine
the number of states before the design of the MCM, and therefore
doubt whether the MCM produced by the k-means algorithm is
optimal. We propose a new MCM based on the genetic algorithm for
recognition applications. Genetic algorithms combine the clustering
algorithm and the MCM design. The users do not need to define the
size of the codebook before the design of the MCM. The genetic
algorithm can automatically find the number of states in MCM, and
thereby obtain a near-optimal MCM. Furthermore, we propose the
fuzzy MCM (FMCM) and the fuzzy genetic algorithm (FGA) to enhance
the recognition rate. Experimental results show that the proposed
MCM outperforms the traditional MCM and other texture and
speech recognition methods.
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