(This work was conducted when the first author was visiting The University of Adelaide, Australia)
Given a query image ‘‘horse", the results returned by one of our hashing method IMH-tSNE with 32 hash bits. False positive returns are marked with red borders.
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 that preserve the Euclidean distance 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 complexity 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 work, we consider how to learn compact binary embeddings on their intrinsic manifolds. In order to address the above-mentioned difficulties, we describe an efficient, inductive solution to the out-of-sample data problem, and a process by which non-parametric manifold learning may be used as the basis of a hashing method. Our proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. We particularly show that hashing on the basis of t-SNE of van der Maaten and Hinton (2008).
Inductive hashing on manifolds.
F. Shen, C. Shen, Q. Shi, A. van den Hengel, Z. Tang. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2013.
Learning hash functions using column generation.
X. Li, G. Lin, C. Shen, A. van den Hengel, A. Dick. International Conf. Machine Learning (ICML), 2013.
C. Shen's participation in this research was supported by Australian Research Council Future Fellowship FT120100969.
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