title: Iterative local-global energy minimization for automatic extraction of objects of interest.
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.
authors.affiliation: Microsoft Live Labs, One Microsoft Way, Redmond, WA 98052, USA. ganghua@microsoft.com
authors.forename: Gang
authors.initials: G
authors.lastname: Hua
authors.forename: Zicheng
authors.initials: Z
authors.lastname: Liu
authors.forename: Zhengyou
authors.initials: Z
authors.lastname: Zhang
authors.forename: Ying
authors.initials: Y
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity