Structural Sensivity for Large-Scale Line-Pattern Recognition
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Discovering Shape Categories by Clustering Shock Trees
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Fuzzy Relational Distance for Large-Scale Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Image retrieval using augmented block truncation coding techniques
Proceedings of the International Conference on Advances in Computing, Communication and Control
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This paper is concerned with the retrieval of images from large databases based on their shape similarity to a query image. Our approach is based on two dimensional histograms that encode both the local and global geometric properties of the shapes. The pairwise attributes are the directed segment relative angle and directed relative position The novelty of the proposed approach is to simultaneously use the relational and structural constraints, derived from an adjacency graph, to gate histogram contributions. We investiguate the retrieval cap abilities of the method for various queries. We also investigate the robustness of the method to segmentation errors. We conclude that a relational histo gram of pairwise segment attributespresents a very efficient way of indexing into large databases. The optimal configuration is obtained when the local features are constructed from six neighbouring segments pairs. Moreover, a sensitivity analysis reveals that segmentation errors do not affect the retrieval performances.