A Heuristic Based on the Intrinsic Dimensionality for Reducing the Number of Cyclic DTW Comparisons in Shape Classification and Retrieval Using AESA
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comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/54899
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INVESTIGACIONMetadata
Title
A Heuristic Based on the Intrinsic Dimensionality for Reducing the Number of Cyclic DTW Comparisons in Shape Classification and Retrieval Using AESADate
2012Publisher
Springer Berlin HeidelbergISBN
978-3-642-34165-6ISSN
0302-9743; 1611-3349Bibliographic citation
Palazón-González, Vicente; Andrés Mazal "A Heuristic Based on the Intrinsic Dimensionality for Reducing the Number of Cyclic DTW Comparisons in Shape Classification and Retrieval Using AESA". En: Structural, Syntactic, and Statistical Pattern Recognition– Joint IAPR International Workshop, SSPR & SPR 2012, Hiroshima, Japan, November 7-9, 2012, Proceedings / Gimel´farb, G. [et al.] (Eds.). Berlin : Springer, 2012. (Lecture Notes in Computer Science; 7626). ISBN 978-3-642-34165-6, pp. 548-556Type
info:eu-repo/semantics/bookPartPublisher version
http://link.springer.com/chapter/10.1007%2F978-3-642-34166-3_60#Subject
Abstract
Cyclic Dynamic Time Warping (CDTW) is a good dissimilarity of shape descriptors of high dimensionality based on contours, but it is computationally expensive. For this reason, to perform recognition tasks, a method ... [+]
Cyclic Dynamic Time Warping (CDTW) is a good dissimilarity of shape descriptors of high dimensionality based on contours, but it is computationally expensive. For this reason, to perform recognition tasks, a method to reduce the number of comparisons and avoid an exhaustive search is convenient. The Approximate and Eliminate Search Algorithm (AESA) is a relevant indexing method because of its drastic reduction of comparisons, however, this algorithm requires a metric distance and that is not the case of CDTW. In this paper, we introduce a heuristic based on the intrinsic dimensionality that allows to use CDTW and AESA together in classification and retrieval tasks over these shape descriptors. Experimental results show that, for descriptors of high dimensionality, our proposal is optimal in practice and significantly outperforms an exhaustive search, which is the only alternative for them and CDTW in these tasks. [-]
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© Springer, Part of Springer Science+Business Media
info:eu-repo/semantics/openAccess
© Springer, Part of Springer Science+Business Media
info:eu-repo/semantics/openAccess