Texture segmentation using vector-valued Chan-Vese model driven by local histogram

Authors:
Yuanquan Wang;Huaibin Wang;Yan Xu
Affiliations:
The Tianjin Key Lab of Novel Software and Intelligent Computing, Tianjin University of Technology, Tianjin 300384, China;The Tianjin Key Lab of Novel Software and Intelligent Computing, Tianjin University of Technology, Tianjin 300384, China;The School of Mechanics Engineering, Tianjin University of Technology, Tianjin 300384, China
Venue:
Computers and Electrical Engineering
Year:
2013

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Abstract

A novel region-based active contour is proposed for texture segmentation. The proposed method is based on the vector-valued Chan-Vese model and local histogram, and the Wasserstein distance is employed to measure the distance between two histograms. Since the histogram is a powerful tool to characterize texture, the proposed method behaves effectively to segment different texture region. Moreover, a Bayesian method is adopted to determine an optimal number of bins in the histogram, so that the computation load can be reduced considerably whilst the effectiveness of histogram to represent texture remains unchanged. Experiments and comparison are conducted and the results show that the proposed strategy is effective for texture segmentation.