Improving bag-of-features for large scale image search

Hervé Jégou 1, * Matthijs Douze 2 Cordelia Schmid 2
* Corresponding author
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
2 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : This article improves recent methods for large scale image search. We first analyze the bag-of-features approach in the framework of approximate nearest neighbor search. This leads us to derive a more precise representation based on Hamming embedding (HE) and 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 in the dataset. We then introduce a graph-structured quantizer which significantly speeds up the assignment of the descriptors to visual words. A comparison with the state of the art shows the interest of our approach when high accuracy is needed. Experiments performed on three reference datasets and 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 shown to be complementary to our weak geometric consistency constraints. Our approach is shown to outperform the state-of-the-art on the three datasets.
Document type :
Journal articles
Liste complète des métadonnées

Cited literature [11 references]  Display  Hide  Download


https://hal.inria.fr/inria-00514760
Contributor : Patrick Gros <>
Submitted on : Wednesday, March 16, 2011 - 3:08:21 PM
Last modification on : Monday, December 17, 2018 - 11:22:02 AM
Document(s) archivé(s) le : Thursday, November 8, 2012 - 11:55:33 AM

Files

jegou_improvingbof_preprint.pd...
Files produced by the author(s)

Identifiers

Citation

Hervé Jégou, Matthijs Douze, Cordelia Schmid. Improving bag-of-features for large scale image search. International Journal of Computer Vision, Springer Verlag, 2010, 87 (3), pp.316-336. ⟨http://www.springerlink.com/content/wh52x87315697752/fulltext.pdf⟩. ⟨10.1007/s11263-009-0285-2⟩. ⟨inria-00514760⟩

Share

Metrics

Record views

3347

Files downloads

2853