Multimedia Tools and Applications

, Volume 74, Issue 24, pp 11569–11594 | Cite as

Approximating adaptive distance measures using scalable feature signatures

  • Jakub LokočEmail author


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.


Similarity search Approximate search Content-based retrieval Adaptive distance measures Scalable descriptor Agglomerative hierarchical clustering 



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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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.SIRET research group, Department of Software Engineering, Faculty of Mathematics and PhysicsCharles University in PraguePragueCzech Republic

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