Représentation compacte des sacs de mots pour l'indexation d'images

Hervé Jégou 1 Matthijs Douze 2 Cordelia Schmid 2
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : One of the main limitations of image search based on bag-of-features is the memory usage per image. Only a few million images can be handled on a single machine in reasonable response time. In this paper, we first evaluate how the memory usage is reduced by using lossless index compression. We then propose an approximate representation of bag-of-features obtained by projecting the corresponding histogram onto a set of pre-defined 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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Hervé Jégou, Matthijs Douze, Cordelia Schmid. Représentation compacte des sacs de mots pour l'indexation d'images. RFIA 2010 - Reconnaissance des Formes et Intelligence Artificielle, Université de Caen Basse-Normandie, Jan 2010, Caen, France. ⟨inria-00548638⟩

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