A new image segmentation technique using maximum spanning tree

Authors:
Qiang He;Chee-Hung Henry Chu
Affiliations:
Department of Mathematics, Computer and Information Sciences, Mississippi Valley State University, Itta Bena, MS;The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA
Venue:
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Year:
2008

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Abstract

An alternative to the gradient-based image segmentation methods are those methods that use eigenvectors based on an affinity matrix built from pairwise pixel similarity. In this paper, we describe a new image segmentation algorithm using the maximum spanning tree. Our method works on the affinity matrix; however, instead of computing eigenvalues and eigenvectors, we show that image segmentation could be transformed into an optimization problem: finding the maximum spanning tree of the graph with image pixels as vertices and pairwise similarities as weights. The experimental results on synthetic and real data show good performance of this algorithm.