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Aggregating Local Image Descriptors into Compact Codes

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6 Author(s)

This paper addresses the problem of large-scale image search. Three constraints have to be taken into account: search accuracy, efficiency, and memory usage. We first present and evaluate different ways of aggregating local image descriptors into a vector and show that the Fisher kernel achieves better performance than the reference bag-of-visual words approach for any given vector dimension. We then jointly optimize dimensionality reduction and indexing in order to obtain a precise vector comparison as well as a compact representation. The evaluation shows that the image representation can be reduced to a few dozen bytes while preserving high accuracy. Searching a 100 million image data set takes about 250 ms on one processor core.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:34 ,  Issue: 9 )

Date of Publication:

Sept. 2012

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