Learning to Hash With Optimized Anchor Embedding for Scalable Retrieval

Guo, Yuchen, Ding, Guiguang, Liu, Li, Han, Jungong and Shao, Ling (2017) Learning to Hash With Optimized Anchor Embedding for Scalable Retrieval. IEEE Transactions on Image Processing, 26 (3). pp. 1344-1354. ISSN 1057-7149

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Official URL: https://doi.org/10.1109/TIP.2017.2652730

Abstract

Sparse representation and image hashing are powerful tools for data representation and image retrieval respectively. The combinations of these two tools for scalable image retrieval, i.e., sparse hashing (SH) methods, have been proposed in recent years and the preliminary results are promising. The core of those methods is a scheme that can efficiently embed the (high-dimensional) image features into a low-dimensional Hamming space, while preserving the similarity between features. Existing SH methods mostly focus on finding better sparse representations of images in the hash space. We argue that the anchor set utilized in sparse representation is also crucial, which was unfortunately underestimated by the prior art. To this end, we propose a novel SH method that optimizes the integration of the anchors, such that the features can be better embedded and binarized, termed as Sparse Hashing with Optimized Anchor Embedding. The central idea is to push the anchors far from the axis while preserving their relative positions so as to generate similar hashcodes for neighboring features. We formulate this idea as an orthogonality constrained maximization problem and an efficient and novel optimization framework is systematically exploited. Extensive experiments on five benchmark image data sets demonstrate that our method outperforms several state-of-the-art related methods.

Item Type: Article
Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Uncontrolled Keywords: sparse representation, hashing, retrieval, scalability, orthogonality, optimization
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ay Okpokam
Date Deposited: 27 Mar 2017 10:44
Last Modified: 11 Sep 2017 18:01
URI: http://nrl.northumbria.ac.uk/id/eprint/30191

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