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Conference papers

A contextual dissimilarity measure for accurate and efficient image search

Hervé Jégou 1 Harzallah Hedi 1 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : In this paper we present two contributions to improve accuracy and speed of an image search system based on bag-of-features: a contextual dissimilarity measure (CDM) and an efficient search structure for visual word vectors. Our measure (CDM) takes into account the local distribution of the vectors and iteratively estimates distance correcting terms. These terms are subsequently used to update an existing distance, thereby modifying the neighborhood structure. Experimental results on the Nister-Stewenius dataset show that our approach significantly outperforms the state-of-the-art in terms of accuracy. Our efficient search structure for visual word vectors is a two-level scheme using inverted files. The first level partitions the image set into clusters of images. At query time, only a subset of clusters of the second level has to be searched. This method allows fast querying in large sets of images. We valuate the gain in speed and the loss in accuracy on large datasets (up to 1 million images).
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Contributor : Hervé Jégou <>
Submitted on : Tuesday, March 15, 2011 - 2:38:06 PM
Last modification on : Tuesday, February 9, 2021 - 3:16:02 PM
Long-term archiving on: : Thursday, June 16, 2011 - 2:20:08 AM

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Hervé Jégou, Harzallah Hedi, Cordelia Schmid. A contextual dissimilarity measure for accurate and efficient image search. CVPR 2007 - Conference on Computer Vision & Pattern Recognition, Jun 2007, Minneapolis, United States. pp.1-8, ⟨10.1109/CVPR.2007.382970⟩. ⟨inria-00394210⟩

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