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Cited by 2,692

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Aggregating local descriptors into a compact image representation

Open access
Jegou, Herve, Douze, Matthijs, Schmid, Cordelia, Perez, Patrick·2010·2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Abstract

We address the problem of image search on a very large scale, where three constraints have to be considered jointly: the accuracy of the search, its efficiency, and the memory usage of the representation. We first propose a simple yet efficient way of aggregating local image descriptors into a vector of limited dimension, which can be viewed as a simplification of the Fisher kernel representation. We then show how to jointly optimize the dimension reduction and the indexing algorithm, so that it best preserves the quality of vector comparison. The evaluation shows that our approach significantly outperforms the state of the art: the search accuracy is comparable to the bag-of-features approach for an image representation that fits in 20 bytes. Searching a 10 million image dataset takes about 50ms.

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Metadata

Authors
Jegou, H., Douze, M., Schmid, C., & Perez, P.
Venue
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2010-06
Pages
3304-3311
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2012592962
Cited by
2,692
References
7
FWCI
66.80
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Aggregating local descriptors into a compact image representation. https://app.cochat.ai/papers/10.1109/cvpr.2010.5540039
The paper
Jegou, H., Douze, M., Schmid, C., & Perez, P. (2010). Aggregating local descriptors into a compact image representation. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2010.5540039

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.