Iterative local-global energy minimization for automatic extraction of objects of interest.
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.
Hua
Gang
G
Microsoft Live Labs, One Microsoft Way, Redmond, WA 98052, USA. ganghua@microsoft.com
Liu
Zicheng
Z
Zhang
Zhengyou
Z
Wu
Ying
Y
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