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

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4 Author(s)
Gang Hua ; Microsoft Live Labs, One Microsoft Way, Redmond, WA ; Zicheng Liu ; Zhengyou Zhang ; Ying Wu

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

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:28 ,  Issue: 10 )