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Fuzzy cluster ensemble and its application on 3D head model classification

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4 Author(s)
Zhiwen Yu ; Dept. of Comput. Sci., City Univ. of Hong Kong, Hong Kong ; Zhongkai Deng ; Hau-San Wong ; Xing Wang

In this paper, we propose a new algorithm called fuzzy cluster ensemble algorithm (FCEA) which integrates the fuzzy logic theory and traditional cluster ensembles for 3D head model classification. Specifically, FCEA consists of two parts: (i) data processing on the distributed locations and (ii) data fusion on the centralized location. In the distributed locations, data processing includes (i) extracting feature vectors from 3D head models, (ii) performing basic fuzzy clustering algorithm to obtain fuzzy membership matrix, while data fusion on the centralized location contains (i) creating a fuzzy cluster ensemble constructor by integrating different fuzzy membership matrices from the distributed locations, and (ii) obtaining the final results of 3D head model classification based on the fuzzy logic theory and the fuzzy cluster ensemble constructor. The experiments show that FCEA works well on 3D head model database.

Published in:

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Date of Conference:

1-8 June 2008