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

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4 Author(s)

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

Published in:

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