A Heuristic Based on the Intrinsic Dimensionality for Reducing the Number of Cyclic DTW Comparisons in Shape Classification and Retrieval Using AESA

  • Vicente Palazón-González
  • Andrés Marzal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)


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.


Cyclic strings cyclic sequences cyclic dynamic time warping shape classification shape retrieval intrinsic dimensionality metric spaces AESA 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Vicente Palazón-González
    • 1
  • Andrés Marzal
    • 1
  1. 1.Dept. Llenguatges i Sistemes Informàtics and Institute of New Imaging TechnologiesUniversitat Jaume I de CastellóSpain

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