Packing bag-of-features

Hervé Jégou 1, 2 Matthijs Douze 1 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : One of the main limitations of image search based on bag-of-features is the memory usage per image, limiting to a few million the size of the dataset that can be handled on a single machine in a reasonable response time. In this paper, we first show that these limitations can be somewhat reduced by using index compression. Then, we propose an image representation obtained by projecting bag-of-features histograms onto a set of predefined sparse projection functions, producing several image descriptors. Coupled with a proper indexing structure, an image is represented by a few hundred bytes. A distance expectation criterion is then used to rank the images. Our method is at least one order of magnitude faster than standard bag-of-features while providing excellent search quality.
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Submitted on : Wednesday, February 23, 2011 - 2:48:54 AM
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Hervé Jégou, Matthijs Douze, Cordelia Schmid. Packing bag-of-features. ICCV 2009 - 12th International Conference on Computer Vision, Sep 2009, Kyoto, Japan. pp.2357-2364, ⟨10.1109/ICCV.2009.5459419⟩. ⟨inria-00394213⟩



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