A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
The essence of constraint propagation
Theoretical Computer Science
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical statistical models for the fusion of multiresolution image data
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Parsing: Unifying Segmentation, Detection, and Recognition
International Journal of Computer Vision
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
Combined Top-Down/Bottom-Up Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting
International Journal of Computer Vision
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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In this paper, we incorporate shape detection into contextual scene labeling and make use of both shape, texture, and context information in a graphical representation. We propose a candidacy graph, whose vertices are two types of recognition candidates for either a superpixel or a window patch. The superpixel candidates are generated by a discriminative classifier with textural features as well as the window proposals by a learned deformable templates model in the bottom-up steps. The contextual and competitive interactions between graph vertices, in form of probabilistic connecting edges, are defined by two types of contextual metrics and the overlapping of their image domain, respectively. With this representation, a composite clustering sampling algorithm is proposed to fast search the optimal convergence globally using the Markov Chain Monte Carlo (MCMC). Our approach is applied on both lotus hill institute (LHI) and MSRC public datasets and achieves the state-of-art results.