High Dimensional Feature Indexing Using Hybrid Trees (1999)
| Venue: | Proc. of the 15th IEEE International Conference on Data Engineering (ICDE |
| Citations: | 9 - 4 self |
BibTeX
@INPROCEEDINGS{Chakrabarti99highdimensional,
author = {Kaushik Chakrabarti and Sharad Mehrotra},
title = {High Dimensional Feature Indexing Using Hybrid Trees},
booktitle = {Proc. of the 15th IEEE International Conference on Data Engineering (ICDE},
year = {1999}
}
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Abstract
Feature based similarity search is emerging as an important search paradigm in database systems. The technique used is to map the data items as points into a high dimensional feature space which is indexed using a multidimensional data structure. Similarity search then corresponds to a range search over the data structure. Traditional multidimensional data structures (e.g., R-tree, KDB-tree, grid files) are of limited use for feature indexing since (1), their performance deteriorates rapidly with the increase in the dimensionality of the feature space(referred to as the "dimensionality curse") and (2), they do not support range queries based on arbitrary distance functions, a situation that occurs commonly in multimedia feature spaces. This paper introduces the hybrid tree -- a multidimensional data structure for indexing high dimensional feature spaces. The hybrid tree combines positive aspects of bounding region (BR)-based data structures (e.g., Rtree, SS-tree, SR-tree) and space par...
