Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Hashing on nonlinear manifolds|
Van Den Hengel, A.
|Citation:||IEEE Transactions on Image Processing, 2015; 24(6):1839-1851|
|Publisher:||Institute of Electrical and Electronics Engineers|
|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|
|Appears in Collections:||Computer Science publications|
Files in This Item:
|RA_hdl_92977.pdf||Restricted Access||6.48 MB||Adobe PDF||View/Open|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.