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Approximating adaptive distance measures using scalable feature signatures

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An Erratum to this article was published on 28 April 2015

Abstract

The feature signatures in connection with the adaptive distance measures have become a respected similarity model for effective multimedia retrieval. However, the efficiency of the model is still a challenging task because the adaptive distance measures have at least quadratic time complexity according to the number of tuples in feature signatures. In order to reduce the number of tuples in feature signatures, we introduce the scalable feature signatures, a new formal framework enabling definition of new methods based on agglomerative hierarchical clustering. We show the framework can be used to express nontrivial feature signature reduction techniques including also popular agglomerative hierarchical clustering techniques. We experimentally demonstrate our new feature signature reduction techniques can be used to implement order of magnitude faster yet effective filter distances approximating the original adaptive distance measures. We also show the filter distances using our new feature signature reduction techniques can compete or even outperform the filter distances based on the related feature signature reduction techniques.

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Notes

  1. Position 〈x,y〉, color 〈L,a,b〉 and texture information 〈c o n t r a s t,e n t r o p y〉, \(\mathbb {F} \subseteq \mathbb {R}^{7}\)

  2. The maximal component feature signatures, centroid linkage method and Ward’s method do not assume a total ordering of the tuples comprising also weights and the lexicographic ordering.

  3. The SQFD parameter α=1.28 performed sufficiently well for the TWIC and Profimedia datasets. However, for the ALOI dataset the effectiveness of all the reduction methods could be improved by SQFD parameter α for small number of tuples and thus for ALOI we have run the experiments also for α∈{0.001,0.08,0.16,0.32,0.64,1.28}.

  4. Profimedia dataset is already provided with a set of query objects.

  5. We utilize an optimized version using precomputed self-similarity matrices.

  6. More comprehensive and rigorous correlation analysis involves inner workings of the utilized adaptive distance measures which is out of the scope of this paper.

  7. The employed GPU extractor uses adaptive k-means that removes too small tuples and merges too similar tuples to reduce the size of the resulting feature signatures.

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Acknowledgments

This research has been supported by Czech Science Foundation project GAČR P202/12/P297. I would also like to thank prof. Mathias Lux for his help with experiments regarding LIRE framework and provided detailed descriptions of utilized LIRE feature histograms and distance measures.

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  1. SIRET research group, Department of Software Engineering, Faculty of Mathematics and Physics, Charles University in Prague, Prague, Czech Republic

    Jakub Lokoč

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Lokoč, J. Approximating adaptive distance measures using scalable feature signatures. Multimed Tools Appl 74, 11569–11594 (2015). https://doi.org/10.1007/s11042-014-2251-4

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