Iconic indexing by 2-D strings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical representations of collections of small rectangles
ACM Computing Surveys (CSUR)
Index-based object recognition in pictorial data management
Computer Vision, Graphics, and Image Processing
Periodicity, Directionality, and Randomness: Wold Features for Image Modeling and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Image Retrieval by Elastic Matching of User Sketches
IEEE Transactions on Pattern Analysis and Machine Intelligence
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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Modern visual information retrieval systems support retrieval by directly addressing image visual features such as color, texture, shape and spatial relationships. However, combining useful representations and similarity models with effcient index structures is a problem that has been largely underestimated. This problem is particularly challenging in the case of retrieval by shape similarity. In this paper we discuss retrieval by shape similarity, using local features and metric indexing. Shape is partitioned into tokens in correspondence with its protrusions, and each token is modeled by a set of perceptually salient attributes. Two distinct distance functions are used to model token similarity and shape similarity. Shape indexing is obtained by arranging tokens into a M-tree index structure. Examples from a prototype system are expounded with considerations about the effectiveness of the approach.