Learning in graphical models
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Incorporating Background Invariance into Feature-Based Object Recognition
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Biologically motivated perceptual feature: generalized robust invariant feature
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Indoor scene classification using combined 3D and gist features
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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In this paper, we present a novel scene interpretation method by unified modeling of visual context using a hierarchical graphical model. Scene interpretation through object recognition is difficult due to several sources of ambiguity (blur, clutter). We model the visual context of scene, object, and part to disambiguate them during recognition. A precisely designed hierarchical graphical model can represent the contexts in a unified way. We also propose a new inference method, particle-based belief propagation, optimized to scene interpretation in this hierarchical graphical model. Such an inference method suits the high-level context of scene interpretation. In addition, our core inference is so general that it can be used in any complex inference problems. Experimental results validate the power of the proposed model of visual context to solve the ambiguities in scene interpretation.