Shock Graphs and Shape Matching
International Journal of Computer Vision
On the Representation and Matching of Qualitative Shape at Multiple Scales
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
View-Based 3-D Object Recognition using Shock Graphs
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
The Earth Mover's Distance under Transformation Sets
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Indexing Hierarchical Structures Using Graph Spectra
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generic Model Abstraction from Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Many-to-many graph matching via metric embedding
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Retrieval of objects in video by similarity based on graph matching
Pattern Recognition Letters
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Object recognition systems have their roots in the AI community, and originally addressed the problem of object categorization. These early systems, however, were limited by their inability to bridge the representational gap between low-level image features and high-level object models, hindered by the assumption of one-to-one correspondence between image and model features. Over the next thirty years, the mainstream recognition community moved steadily in the direction of exemplar recognition while narrowing the representational gap. The community is now returning to the categorization problem, and faces the same representational gap as its predecessors did. We review the evolution of object recognition systems and argue that bridging this representational gap requires an ability to match image and model features many-to-many. We review three formulations of the many-to-many matching problem as applied to model acquisition and object recognition.