A novel initialization scheme for the fuzzy c-means algorithm for color clustering

Authors:
Dae-Won Kim;Kwang H. Lee;Doheon Lee
Affiliations:
Department of Electrical Engineering and Computer Science, KAIST, 373-1, Kusung-dong, Yusung-gu, Daejeon 305701, South Korea;Department of Electrical Engineering and Computer Science, KAIST, 373-1, Kusung-dong, Yusung-gu, Daejeon 305701, South Korea and Department of BioSystems, KAIST, 373-1, Kusung-dong, Yusung-gu, Dae ...;Department of BioSystems, KAIST, 373-1, Kusung-dong, Yusung-gu, Daejeon 305701, South Korea
Venue:
Pattern Recognition Letters
Year:
2004

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Abstract

A novel initialization scheme for the fuzzy c-means (FCM) algorithm is proposed for the color clustering problem. Given a set of color points, the proposed initialization scheme extracts the most vivid and distinguishable colors, referred to here as the dominant colors. The color points closest to these dominant colors are selected as the initial centroids in the FCM calculations. To obtain the dominant colors and their closest color points, we introduce reference colors and define a fuzzy membership model between a color point and a reference color. The effectiveness and reliability of the proposed method is demonstrated through various color clustering examples.