Graph matching with hierarchical discrete relaxation
Pattern Recognition Letters
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Feature Hierarchies for Object Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
International Journal of Computer Vision
Fast and cheap object recognition by linear combination of views
Proceedings of the 6th ACM international conference on Image and video retrieval
Feature Correspondence Via Graph Matching: Models and Global Optimization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
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A pseudo-hierarchical graph matching procedure dedicated to object recognition is presented in this paper. From a single model image, a graph is built by extracting invariant local features and linking them according to a so-called proximity rule. The resulting graph presents several interesting properties including invariance to scale, robustness to various distortions and empirical linearity of the number of edges with respect to the number of nodes. The matching process is made hierarchical in order to increase both speed and detection performances. It relies on progressively incorporating the smaller model features as the hierarchy level increases. As a result, even a matching between graphs containing thousands of nodes is very fast (a few milliseconds). Experiments demonstrates that the method outperforms state-of-the-art specific object detectors in terms of precision-recall measures and detection time.