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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 671397, 13 pages
http://dx.doi.org/10.1155/2012/671397
Research Article

Contextual Hierarchical Part-Driven Conditional Random Field Model for Object Category Detection

College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha 410073, China

Received 25 October 2012; Accepted 11 November 2012

Academic Editor: Sheng-yong Chen

Copyright © 2012 Lizhen Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Even though several promising approaches have been proposed in the literature, generic category-level object detection is still challenging due to high intraclass variability and ambiguity in the appearance among different object instances. From the view of constructing object models, the balance between flexibility and discrimination must be taken into consideration. Motivated by these demands, we propose a novel contextual hierarchical part-driven conditional random field (CRF) model, which is based on not only individual object part appearance but also model contextual interactions of the parts simultaneously. By using a latent two-layer hierarchical formulation of labels and a weighted neighborhood structure, the model can effectively encode the dependencies among object parts. Meanwhile, beta-stable local features are introduced as observed data to ensure the discriminative and robustness of part description. The object category detection problem can be solved in a probabilistic framework using a supervised learning method based on maximum a posteriori (MAP) estimation. The benefits of the proposed model are demonstrated on the standard dataset and satellite images.