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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 1st International Conference on Robot, Vision and Signal Processing, RVSP 2011
Pages48-51
Number of pages4
DOIs
Publication statusPublished - 2011 Dec 1
Event1st International Conference on Robot, Vision and Signal Processing, RVSP 2011 - Kaohsiung, Taiwan
Duration: 2011 Nov 212011 Nov 23

Publication series

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

Other

Other1st International Conference on Robot, Vision and Signal Processing, RVSP 2011
CountryTaiwan
CityKaohsiung
Period11-11-2111-11-23

All Science Journal Classification (ASJC) codes

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

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  • Cite this

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