Multi-criteria Search Algorithm: An Efficient Approximate K-NN Algorithm for Image Retrieval

Mehdi Badr 1 Dan Vodislav 1 David Picard 1 Shaoyi Yin 1 Philippe-Henri Gosselin 1, 2
1 MIDI
ETIS - Equipes Traitement de l'Information et Systèmes
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We propose a new method for approximate k-NN search in large scale image databases, based on top-k multi-criteria search techniques. The method defines a simple index structure based on sorted lists, which provides a good compromise between fast retrieval, storage requirements and update cost. The search algorithm delivers approximate results with guarantees about false negatives, with fast emergence of good approximations, monotonically improved and leading if necessary to an exact result. Experiments with the on-disk implementation show that our method produces very good approximate results several times faster than the Baseline method.
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Mehdi Badr, Dan Vodislav, David Picard, Shaoyi Yin, Philippe-Henri Gosselin. Multi-criteria Search Algorithm: An Efficient Approximate K-NN Algorithm for Image Retrieval. IEEE Int. Conf. on Image Processing ICIP2013, Sep 2013, Melbourne, Australia. pp.2901-2905, ⟨10.1109/ICIP.2013.6738597⟩. ⟨hal-00832196v2⟩

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