Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search

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.

DOI: 10.1007/978-3-540-88682-2_24

Extracted Key Phrases

8 Figures and Tables

01002002008200920102011201220132014201520162017
Citations per Year

1,321 Citations

Semantic Scholar estimates that this publication has 1,321 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Jgou2008HammingEA, title={Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search}, author={Herv{\'e} J{\'e}gou and Matthijs Douze and Cordelia Schmid}, booktitle={ECCV}, year={2008} }