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2008
Springer

Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search

10 years 1 days ago
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
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 suboptimality 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...
Herve Jegou, Matthijs Douze, Cordelia Schmid
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2008
Where ECCV
Authors Herve Jegou, Matthijs Douze, Cordelia Schmid
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