inria-00548638, version 1
Représentation compacte des sacs de mots pour l'indexation d'images
Hervé Jégou
a, 1Matthijs Douze a, 2, 3Cordelia Schmid 2, 3
17° congrès Reconnaissance des Formes et Intelligence Artificielle (RFIA '10) (2010)
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
- a – INRIA
- 1: TEXMEX (INRIA - IRISA)
- CNRS : UMR6074 – INRIA – INSA Rennes – Université de Rennes 1
- 2: LEAR (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann)
- CNRS : FR71 – CNRS : UMR5527 – INRIA – Laboratoire Jean Kuntzmann – Université Joseph Fourier - Grenoble I – Institut National Polytechnique de Grenoble (INPG)
- 3: Laboratoire Jean Kuntzmann (LJK)
- CNRS : UMR5224 – Université Joseph Fourier - Grenoble I – Université Pierre Mendès-France - Grenoble II – Institut Polytechnique de Grenoble - Grenoble Institute of Technology
- Domain : Computer Science/Computer Vision and Pattern Recognition
- Keywords : large scale image search – bag-of-features – compression
- inria-00548638, version 1
- http://hal.inria.fr/inria-00548638
- oai:hal.inria.fr:inria-00548638
- From: Team Lear
- Submitted for:
- Submitted on: Monday, 20 December 2010 10:23:10
- Updated on: Monday, 10 January 2011 15:07:10
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