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Elliptical Clustering with Incremental Growth and its Application to Skin Color Region Segmentation  

Lee Kyoung-Mi (덕성여대 컴퓨터과학부)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.9, 2004 , pp. 1161-1170 More about this Journal
Abstract
This paper proposes to segment skin color areas using a clustering algorithm. Most of previously proposed clustering algorithms have some difficulties, since they generally detect hyperspherical clusters, run in a batch mode, and predefine a number of clusters. In this paper, we use a well-known elliptical clustering algorithm, an EM algorithm, and modify it to learn on-line and find automatically the number of clusters, called to an EAM algorithm. The effectiveness of the EAM algorithm is demonstrated on a task of skin color region segmentation. Experimental results present the EAM algorithm automatically finds a right number of clusters in a given image without any information on the number. Comparing with the EM algorithm, we achieved better segmentation results with the EAM algorithm. Successful results were achieved to detect and segment skin color regions using a conditional probability on a region. Also, we applied to classify images with persons and got good classification results.
Keywords
Elliptical clustering; EM algorithm; On-line learning; Resource-allocating algorithm; Skin color region detection;
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