Adapted mean variable distance to fuzzy-cmeans for effective image clustering

S. Ramathilaga, James Jiunn Yin Leu, Yueh Min Huang

研究成果: Conference contribution

5 引文 斯高帕斯(Scopus)

摘要

Fuzzy C-means had been used for data clustering problems for recently years. However, if it uses the non-robust objective function of FCM (Fuzzy C-Means), we will get poor result if data corrupted because some noises. To improve these problems, this paper make effective objective functions of Fuzzy C-means which named MVDFCM (Mean Variable Distance Fuzzy C-means).The method is with center learning method which is on the basis of quadratic mean distance, entropy methods, and regularization terms. Moreover, the center learning method can cut down the computation complexity and running time. The results show the proposed method get more quality to the previous method.

原文English
主出版物標題Proceedings - 1st International Conference on Robot, Vision and Signal Processing, RVSP 2011
頁面48-51
頁數4
DOIs
出版狀態Published - 2011 十二月 1
事件1st International Conference on Robot, Vision and Signal Processing, RVSP 2011 - Kaohsiung, Taiwan
持續時間: 2011 十一月 212011 十一月 23

出版系列

名字Proceedings - 1st International Conference on Robot, Vision and Signal Processing, RVSP 2011

Other

Other1st International Conference on Robot, Vision and Signal Processing, RVSP 2011
國家Taiwan
城市Kaohsiung
期間11-11-2111-11-23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • 引用此

    Ramathilaga, S., Leu, J. J. Y., & Huang, Y. M. (2011). Adapted mean variable distance to fuzzy-cmeans for effective image clustering. 於 Proceedings - 1st International Conference on Robot, Vision and Signal Processing, RVSP 2011 (頁 48-51). [6114892] (Proceedings - 1st International Conference on Robot, Vision and Signal Processing, RVSP 2011). https://doi.org/10.1109/RVSP.2011.58