Packing bag-of-features
Résumé
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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jegou_packingbof.pdf (3.95 Mo)
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indexing.png (25.81 Ko)
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poster_iccv_raster.pdf (11.76 Mo)
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| Origine | Fichiers produits par l'(les) auteur(s) |
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| Format | Figure, Image |
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| Format | Autre |
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