Sim-Min-Hash: An efficient matching technique for linking large image collections

Wan-Lei Zhao 1, * Hervé Jégou 1 Guillaume Gravier 1
* Auteur correspondant
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : One of the most successful method to link all similar images within a large collection is min-Hash, which is a way to significantly speed-up the comparison of images when the underlying image representation is bag-of-words. However, the quantization step of min-Hash introduces important information loss. In this paper, we propose a generalization of min-Hash, called Sim-min-Hash, to compare sets of real-valued vectors. We demonstrate the effectiveness of our approach when combined with the Hamming embedding similarity. Experiments on large-scale popular benchmarks demonstrate that Sim-min-Hash is more accurate and faster than min-Hash for similar image search. Linking a collection of one million images described by 2 billion local descriptors is done in 7 minutes on a single core machine.
Type de document :
Communication dans un congrès
ACM Multimedia, Oct 2013, Barcelona, Spain. ACM, 2013
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https://hal.inria.fr/hal-00839921
Contributeur : Hervé Jégou <>
Soumis le : lundi 5 août 2013 - 11:28:07
Dernière modification le : mercredi 11 avril 2018 - 02:01:10
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Wan-Lei Zhao, Hervé Jégou, Guillaume Gravier. Sim-Min-Hash: An efficient matching technique for linking large image collections. ACM Multimedia, Oct 2013, Barcelona, Spain. ACM, 2013. 〈hal-00839921v3〉

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