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ICIG
2009
IEEE

Discriminative Maximum Margin Image Object Categorization with Exact Inference

10 years 6 months ago
Discriminative Maximum Margin Image Object Categorization with Exact Inference
Categorizing multiple objects in images is essentially a structured prediction problem: the label of an object is in general dependent on the labels of other objects in the image. We explicitly model object dependencies in a sparse graphical topology induced by the adjacency of objects in the image, which benefits inference, and then use maximum margin principle to learn the model discriminatively. Moreover, we propose a novel exact inference method, which is used in training to find the most violated constraint required by cutting plane method. A slightly modified inference method is used in testing when the target labels are unseen. Experiment results on both synthetic and real datasets demonstrate the improvement of the proposed approach over the state-of-the-art methods.
Qinfeng Shi, Luping Zhou, Li Cheng, Dale Schuurman
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICIG
Authors Qinfeng Shi, Luping Zhou, Li Cheng, Dale Schuurmans
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