Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/66734
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Type: Conference paper
Title: Discriminative maximum margin image object categorization with exact inference
Author: Shi, Q.
Zhou, L.
Cheng, L.
Schuurmans, D.
Citation: Proceedings of the 5th International Conference on Image and Graphics, held in Xi'an, China, 20-23 September 2009: pp.232-237
Publisher: IEEE Computer Society
Publisher Place: Los Alamitos, California
Issue Date: 2009
ISBN: 9780769538839
Conference Name: International Conference on Image and Graphics (5th : 2009 : Xi'an, China)
Statement of
Responsibility: 
Qinfeng Shi, Luping Zhou, Li Cheng and Dale Schuurmans
Abstract: 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.
Rights: © 2009 IEEE
RMID: 0020107162
DOI: 10.1109/ICIG.2009.162
Appears in Collections:Computer Science publications

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