Inductive Hashing on Manifolds

By Fumin Shen, Chunhua Shen, Javen Qinfeng Shi, Anton van den Hengel, Zhenmin Tang. 2013

36 citations recorded by the source corpus.

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 [29] outperforms state-of-the-art hashing methods on large-scale benchmark datasets, and is very effective for image classification with very short code lengths.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

  1. Tat-seng Chua
  2. Hanwang Zhang
  3. Dacheng Tao
  4. Jingdong Wang
  5. Wei David Liu
  6. Chunhua Shen
  7. Yi Yang
  8. Luc Van Gool

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