Hamming embedding and weak geometric consistency for large scale image search

Hervé Jégou 1 Matthijs Douze 1 Cordelia Schmid 1
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : This paper improves recent methods 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 the 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.
Document type :
Conference papers
Complete list of metadatas

Contributor : Hervé Jégou <>
Submitted on : Tuesday, March 15, 2011 - 2:43:52 PM
Last modification on : Tuesday, June 18, 2019 - 3:38:04 PM
Long-term archiving on : Thursday, November 8, 2012 - 11:45:10 AM




Hervé Jégou, Matthijs Douze, Cordelia Schmid. Hamming embedding and weak geometric consistency for large scale image search. ECCV 2008 - 10th European Conference on Computer Vision, Oct 2008, Marseille, France. pp.304-317, ⟨10.1007/978-3-540-88682-2_24⟩. ⟨inria-00316866⟩



Record views


Files downloads