Optimized Projection for Hashing

Chu, Chaoqun and Gong, Dahan and Chen, Kai and Guo, Yuchen and Han, Jungong and Ding, Guiguang (2019) Optimized Projection for Hashing. Pattern Recognition Letters, 117. pp. 169-178. ISSN 0167-8655

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Abstract

Hashing, which seeks for binary codes to represent data, has drawn increasing research interest in recent years. Most existing Hashing methods follow a projection-quantization framework which first projects high-dimensional data into compact low-dimensional space and then quantifies the compact data into binary codes. The projection step plays a key role in Hashing and academia has paid considerable attention to it. Previous works have proven that a good projection should simultaneously 1) preserve important information in original data, and 2) lead to compact representation with low quantization error. However, they adopted a greedy two-step strategy to consider the above two properties separately. In this paper, we empirically show that such a two-step strategy will result in a sub-optimal solution because the optimal solution to 1) limits the feasible set for the solution to 2). We put forward a novel projection learning method for Hashing, dubbed Optimized Projection (OPH). Specifically, we propose to learn the projection in a unified formulation which can find a good trade-off such that the overall performance can be optimized. A general framework is given such that OPH can be incorporated with different Hashing methods for different situations. We also introduce an effective gradient-based optimization algorithm for OPH. We carried out extensive experiments for Hashing-based Approximate Nearest Neighbor search and Content-based Data Retrieval on six benchmark datasets. The results show that OPH significantly outperforms several state-of-the-art related Hashing methods.

Item Type:
Journal Article
Journal or Publication Title:
Pattern Recognition Letters
Additional Information:
This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters, 117, 2019 https://www.sciencedirect.com/journal/pattern-recognition-letters
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1707
Subjects:
ID Code:
124791
Deposited By:
Deposited On:
20 Apr 2018 14:10
Refereed?:
Yes
Published?:
Published
Last Modified:
25 Nov 2020 05:21