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Multi-Phase Texture Segmentation Using Gabor Features Histograms Based on Wasserstein Distance

Published online by Cambridge University Press:  03 June 2015

Motong Qiao ,
Wei Wang  and
Michael Ng
Motong Qiao*
Affiliation:
Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Wei Wang
Affiliation:
Department of Mathematics, Tongji University, Shanghai 200092, China
Michael Ng
Affiliation:
Centre for Mathematical Imaging and Vision and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
*
*Corresponding author.Email:qiao.motong@gmail.com
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Abstract

We present a multi-phase image segmentation method based on the histogram of the Gabor feature space, which consists of a set of Gabor-filter responses with various orientations, scales and frequencies. Our model replaces the error function term in the original fuzzy region competition model with squared 2-Wasserstein distance function, which is a metric to measure the distance of two histograms. The energy functional is minimized by alternative minimization method and the existence of closed-form solutions is guaranteed when the exponent of the fuzzy membership term being 1 or 2. We test our model on both simple synthetic texture images and complex natural images with two or more phases. Experimental results are shown and compared to other recent results.

Keywords

68U10Multi-phase texture segmentationWasserstein distanceGabor filterMumford-Shah model
Type
Research Article
Information
Communications in Computational Physics , Volume 15 , Issue 5 , May 2014 , pp. 1480 - 1500
Copyright
Copyright © Global Science Press Limited 2014

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