Efficient Learning to Label Images
Jia, Ke; Cheng, Li; Liu, Nianjun; Wang, Lei
Conditional random field methods (CRFs) have gained popularity for image labeling tasks in recent years. In this paper, we describe an alternative discriminative approach, by extending the large margin principle to incorporate spatial correlations among neighboring pixels. In particular, by explicitly enforcing the submodular condition, graph-cuts is conveniently integrated as the inference engine to attain the optimal label assignment efficiently. Our approach allows learning a model with...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of The 23rd IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010)|
|01_Jia_Efficient_Learning_to_Label_2010.pdf||362.72 kB||Adobe PDF||Request a copy|
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