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Type: Journal article
Title: Hashing on nonlinear manifolds
Author: Shen, F.
Shen, C.
Shi, Q.
Van Den Hengel, A.
Tang, Z.
Shen, H.
Citation: IEEE Transactions on Image Processing, 2015; 24(6):1839-1851
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2015
ISSN: 1057-7149
Statement of
Fumin Shen, Chunhua Shen, Qinfeng Shi, Anton van den Hengel, Zhenmin Tang, and Heng Tao Shen
Abstract: Abstract- Learning-based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these methods are designed to generate binary codes preserving the Euclidean similarity in the original space. Manifold learning techniques, in contrast, are better able to model the intrinsic structure embedded in the original high-dimensional data. The complexities of these models, and the problems with out-of-sample data, have previously rendered them unsuitable for application to large-scale embedding, however. In this paper, how to learn compact binary embeddings on their intrinsic manifolds is considered. In order to address the above-mentioned difficulties, an efficient, inductive solution to the out-of-sample data problem, and a process by which nonparametric manifold learning may be used as the basis of a hashing method are proposed. The proposed approach thus allows the develop- ment of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. It is particularly shown that hashing on the basis of t-distributed stochastic neighbor embedding outperforms state-of-the-art hashing methods on large-scale benchmark data sets, and is very effective for image classification with very short code lengths. It is shown that the proposed framework can be further improved, for example, by minimizing the quantization error with learned orthogonal rotations without much computation overhead. In addition, a supervised inductive manifold hashing framework is developed by incorporating the label information, which is shown to greatly advance the semantic retrieval performance.
Keywords: Hashing; binary code learning; manifold learning; image retrieval.
Rights: © 2015 IEEE
RMID: 0030030840
DOI: 10.1109/TIP.2015.2405340
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Appears in Collections:Computer Science publications

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