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Aggregating local descriptors into a compact image representation

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Aggregating local descriptors into a compact image representation
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.
Hervé Jégou, Matthijs Douze, Cordelia Schmid and
Added 08 Mar 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Hervé Jégou, Matthijs Douze, Cordelia Schmid and Patrick Pérez
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