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| 研究生： |
白仁賢 Jen-Hsien Pai |
|---|---|
| 論文名稱： |
W-K-means algorithms spatial correction W-K-means algorithms spatial correction |
| 指導教授： |
洪文良
Wen-Liang Hung |
| 口試委員: | |
| 學位類別： |
碩士 Master |
| 系所名稱： |
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| 論文出版年： | 2009 |
| 畢業學年度： | 97 |
| 語文別： | 英文 |
| 論文頁數： | 21 |
| 中文關鍵詞： | 彩色影像分割 、K-means演算法 、W-K-means演算法 、模糊分類 、高斯核心的模糊分類 、FCM |
| 外文關鍵詞： | Color image segmentation, k-means algorithm, W-k-means algorithm, Fuzzy clustering, Fuzzy c-means, Gaussian kernel-based FCM |
| 相關次數： | 點閱：1 下載：0 |
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FCM_S對影像分割是一個合適且有效的演算法。Chen and Zhang在2004年用強健的核心方法由FCM_S導出KFCM_S, KFCM_S1 及 KFCM_S2。Huang et al.在2005年提出一個會自動計算多變權重的演算法(W-k-means)。本篇我們提出AWKM_S2 及 RAWKM_S2 演算法，從一些數值和影像的實驗和KFCM_S2做比較，我們發現AWKM_S2 及 RAWKM_S2 演算法可以獲得較好的實驗結果。
Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for image segmentation. Chen and Zhang (2004) propose robust kernelized versions KFCM_S, KFCM_S1 and KFCM_S2 by applying the kernel methods. Huang et al. (2005) proposes a W-k-means algorithm that can automatically calculate variable weights. In this paper we propose W-k-means algorithms with spatial correction, and we call them AWKM_S2 and RAWKM_S2. Some numerical and image experiments are performed to assess the performance of AWKM_S2 and RAWKM_S2 in comparison with KFCM_S2.Experimental results show that the proposed AWKM_S2 and RAWKM_S2 have better performance.
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[8] Miin-Shen Yang, Hsu-Shen Tsai, Pattern Recognition Letters, Volume 29, Issue 12, 1 September 2008, 1713-1725
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