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Paper

AN IMPROVED METHOD TO SELECT CANDIDATES ON METRIC INDEX VP-TREETopics: Data Analytics; Foundations of Knowledge Discovery in Databases; Mining High-Dimensional Data; Structured Data Analysis and Statistical Methods

Keyword(s):Multimedia retrieval systems, Indexing technique, Vantage point tree, Triangule inequality.

Related
Ontology
Subjects/Areas/Topics:Artificial Intelligence
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Foundations of Knowledge Discovery in Databases
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining High-Dimensional Data
;
Structured Data Analysis and Statistical Methods
;
Symbolic Systems

Abstract: On multimedia databases, it is one of important techniques to use the efficient indexing method for the fast access. Metric indexing methods can apply for various distance measures other than the Euclidean distance. Then, metric indexing methods have higher flexibility than multi-dimensional indexing methods. We focus on the Vantage Point tree (VP-tree) which is one of the metric indexing methods. VP-tree is an efficient metric space indexing method, however the number of distance calculations at leaf nodes tends to increase. In this paper, we propose an efficient algorithm to reduce the number of distance calculations at leaf nodes of the VPtree. The conventional VP-tree uses the triangle inequality at the leaf node in order to reduce the number of distance calculations. At this point, the vantage point of the VP-tree is used as a reference point of the triangle inequality. The proposed algorithm uses the nearest neighbor (NN) point for the query instead of the vantage point as the reference point. By using this method, the selection range by the triangle inequality becomes small, and the number of distance calculations at leaf nodes can be cut down. Moreover, it is impossible to specify the NN point in advance. Then, this method regards the nearest point to the query in the result buffer as the temporary NN point. If the nearer point is found on the retrieval process, the temporary NN point is replaced with new one. From evaluation experiments using 10,000 image data, it was found that our proposed method could cut 5%12% of search time of the conventional VP-tree.(More)

On multimedia databases, it is one of important techniques to use the efficient indexing method for the fast access. Metric indexing methods can apply for various distance measures other than the Euclidean distance. Then, metric indexing methods have higher flexibility than multi-dimensional indexing methods. We focus on the Vantage Point tree (VP-tree) which is one of the metric indexing methods. VP-tree is an efficient metric space indexing method, however the number of distance calculations at leaf nodes tends to increase. In this paper, we propose an efficient algorithm to reduce the number of distance calculations at leaf nodes of the VPtree. The conventional VP-tree uses the triangle inequality at the leaf node in order to reduce the number of distance calculations. At this point, the vantage point of the VP-tree is used as a reference point of the triangle inequality. The proposed algorithm uses the nearest neighbor (NN) point for the query instead of the vantage point as the reference point. By using this method, the selection range by the triangle inequality becomes small, and the number of distance calculations at leaf nodes can be cut down. Moreover, it is impossible to specify the NN point in advance. Then, this method regards the nearest point to the query in the result buffer as the temporary NN point. If the nearer point is found on the retrieval process, the temporary NN point is replaced with new one. From evaluation experiments using 10,000 image data, it was found that our proposed method could cut 5%12% of search time of the conventional VP-tree.

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Shishibori, M.; Sangkon Lee, S. and Kita, K. (2011). AN IMPROVED METHOD TO SELECT CANDIDATES ON METRIC INDEX VP-TREE. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - KDIR, (IC3K 2011) ISBN 978-989-8425-79-9; ISSN 2184-3228, pages 306-311. DOI: 10.5220/0003668803140319

@conference{kdir11, author={Masami Shishibori. and Samuel {Sangkon Lee}. and Kenji Kita.}, title={AN IMPROVED METHOD TO SELECT CANDIDATES ON METRIC INDEX VP-TREE}, booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - KDIR, (IC3K 2011)}, year={2011}, pages={306-311}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0003668803140319}, isbn={978-989-8425-79-9}, issn={2184-3228}, }

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - KDIR, (IC3K 2011) TI - AN IMPROVED METHOD TO SELECT CANDIDATES ON METRIC INDEX VP-TREE SN - 978-989-8425-79-9 IS - 2184-3228 AU - Shishibori, M. AU - Sangkon Lee, S. AU - Kita, K. PY - 2011 SP - 306 EP - 311 DO - 10.5220/0003668803140319

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