Iterative Local-Global Energy Minimization for Automatic Extraction of Objects of Interest

Authors:
Gang Hua;Zicheng Liu;Zhengyou Zhang;Ying Wu
Affiliations:
IEEE;IEEE;IEEE;IEEE
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2006

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Abstract

We propose a novel global-local variational energy to automatically extract objects of interest from images. Previous formulations only incorporate local region potentials, which are sensitive to incorrectly classified pixels during iteration. We introduce a global likelihood potential to achieve better estimation of the foreground and background models and, thus, better extraction results. Extensive experiments demonstrate its efficacy.