Learning to Project and Binarise for Hashing Based Approximate Nearest Neighbour Search

Moran, S. (2016) Learning to Project and Binarise for Hashing Based Approximate Nearest Neighbour Search. In: SIGIR 2016, Pisa, Italy, 17-21 Jul 2016, pp. 897-900. ISBN 9781450340694 (doi: 10.1145/2911451.2914766)

Abstract

In this paper we focus on improving the effectiveness of hashing-based approximate nearest neighbour search. Generating similarity preserving hashcodes for images has been shown to be an effective and efficient method for searching through large datasets. Hashcode generation generally involves two steps: bucketing the input feature space with a set of hyperplanes, followed by quantising the projection of the data-points onto the normal vectors to those hyperplanes. This procedure results in the makeup of the hashcodes depending on the positions of the data-points with respect to the hyperplanes in the feature space, allowing a degree of locality to be encoded into the hashcodes. In this paper we study the effect of learning both the hyperplanes and the thresholds as part of the same model. Most previous research either learn the hyperplanes assuming a fixed set of thresholds, or vice-versa. In our experiments over two standard image datasets we find statistically significant increases in retrieval effectiveness versus a host of state-ofthe-art data-dependent and independent hashing models.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Moran, Mr Sean
• Authors: Moran, S.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450340694
• Copyright Holders: Copyright © 2016 The Author
• First Published: First published in Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval: 897-900
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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