A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Texture Segmentation by Multiscale Aggregation of Filter Responses and Shape Elements
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
A Spectral Technique for Correspondence Problems Using Pairwise Constraints
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Local detection of occlusion boundaries in video
Image and Vision Computing
Correspondence propagation with weak priors
IEEE Transactions on Image Processing
The optimal distance measure for object detection
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Probabilistic subgraph matching based on convex relaxation
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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The main difficulty for the recognition of occluded objects lies in the fact that the original feature set is corrupted and no longer reliable to represent the object of interest. This corruption is caused by the interactions between features from different objects, denoted as feature interactions, which is a key issue addressed in our algorithm. In this paper, a local to global strategy is represented for the occlusion recognition problem, which combines the pairwise grouping and graph matching algorithms. Local appearance similarity serves as priors to reduce feature interactions, by which the performance of graph matching algorithms is improved in order to deal with the contaminated data set. With our formulation, a global decision on object recognition can be made based on locally gathered information. Experimental results show that the proposed framework can dramatically reduce incorrect matches and objects under severe occlusions can still be recognized.