Variational color image segmentation via chromaticity-brightness decomposition

Authors:
Zheng Bao;Yajun Liu;Yaxin Peng;Guixu Zhang
Affiliations:
United Data Information Technology Co. Ltd, Shanghai, China;Department of Mathematics, East China Normal University, Shanghai, China;Department of Mathematics, Shanghai University, Shanghai, China;Department of Computer Science, East China Normal University, Shanghai, China
Venue:
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Year:
2010

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Abstract

A region-based variational model for color image segmentation is proposed using the chromaticity-brightness decomposition. By this decomposition, we extend the Wasserstein distance based method to color images. The chromaticity term of the proposed functional follows the data term of the color Chan-Vese model with constraint on unit sphere, and the brightness term is formulated by the Wasserstein distance between the computed probability density function in the local windows (e.g. 3 by 3 or 5 by 5 window) and its estimated counterparts in classified regions. Experimental results on synthetic and real color images show that the proposed method performs well for the segmentation of different image regions.