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Hamming Embedding and Weak Geometry Consistency for Large Scale Image Search - extended version

Hervé Jégou 1 Matthijs Douze 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 : This technical report presents and extends a recent paper we have proposed for large scale image search. State-of-the-art methods build on the bag-of- features image representation. We first analyze bag-of-features in the framework of approximate nearest neighbor search. This shows the sub-optimality of such a representation for matching descriptors and leads us to derive a more precise representation based on 1) Hamming embedding (HE) and 2) weak geometric consistency constraints (WGC). HE provides binary signatures that refine the matching based on visual words. WGC filters matching descriptors that are not consistent in terms of angle and scale. HE and WGC are integrated within an inverted file and are efficiently exploited for all images, even in the case of very large datasets. Experiments performed on a dataset of one million of images show a significant improvement due to the binary signature and the weak geometric consistency constraints, as well as their efficiency. Estimation of the full geometric transformation, i.e., a re-ranking step on a short list of images, is complementary to our weak geometric consistency constraints and allows to further improve the accuracy.
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Submitted on : Monday, December 20, 2010 - 10:24:17 AM
Last modification on : Thursday, January 20, 2022 - 5:30:14 PM
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  • HAL Id : inria-00548651, version 1



Hervé Jégou, Matthijs Douze, Cordelia Schmid. Hamming Embedding and Weak Geometry Consistency for Large Scale Image Search - extended version. [Research Report] 6709, 2008, pp.27. ⟨inria-00548651⟩



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