Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
ALSBIR: A local-structure-based image retrieval
Pattern Recognition
Image retrieval based on indexing and relevance feedback
Pattern Recognition Letters
Information Sciences: an International Journal
A fast all nearest neighbor algorithm for applications involving large point-clouds
Computers and Graphics
CM-tree: A dynamic clustered index for similarity search in metric databases
Data & Knowledge Engineering
Navigating k-nearest neighbor graphs to solve nearest neighbor searches
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Fast approximate similarity search based on degree-reduced neighborhood graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Query-driven iterated neighborhood graph search for large scale indexing
Proceedings of the 20th ACM international conference on Multimedia
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This paper examines the problem of database organization and retrieval based on computing metric pairwise distances. A low-dimensional Euclidean approximation of a high-dimensional metric space is not efficient, while search in a high-dimensional Euclidean space suffers from the "curse of dimensionality". Thus, techniques designed for searching metric spaces must be used. We evaluate several such existing exact metric-based indexing techniques, and show that they require extensive computational effort. This motivates the development of an approximate nearest neighbor search technique where the K nearest neighbors are used to approximate the local neighborhood of a point. The resulting K NN graph is searched in a best-first fashion producing excellent indexing efficiency.