Unsupervised multiphase segmentation: A recursive approach

Authors:
Kangyu Ni;Byung-Woo Hong;Stefano Soatto;Tony Chan
Affiliations:
Department of Mathematics, University of California, Los Angeles, CA, USA;Department of Computer Science, Chung-Ang University, Seoul, Korea;Department of Computer Science, University of California, Los Angeles, CA, USA;Department of Mathematics, University of California, Los Angeles, CA, USA
Venue:
Computer Vision and Image Understanding
Year:
2009

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Abstract

We propose an unsupervised multiphase segmentation algorithm based on Bresson et al.'s fast global minimization of Chan and Vese's two-phase piecewise constant segmentation model. The proposed algorithm recursively partitions a region into two subregions, starting from the largest scale. The segmentation process automatically terminates and detects when all the regions cannot be partitioned further. The number of regions is not given and can be arbitrary. Furthermore, this method provides a full hierarchical representation that gives a structure of a given image.