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Locality Preserving Discriminative Hashing

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Published:03 November 2014Publication History

ABSTRACT

Hashing for large scale similarity search has become more and more popular because of its improvement in computational speed and storage reduction. Semi-supervised Hashing (SSH) has been proven effective since it integrates both labeled and unlabeled data to leverage semantic similarity while keeping robust to overfitting. However, it ignores the global label information and the local structure of the feature space. In this paper, we concentrate on these two issues and propose a novel semi-supervised hashing method called Locality Preserving Discriminative Hashing which combines two classical dimensionality reduction approaches, Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP). The proposed method presents a rigorous formulation in which the supervised term tries to maintain the global information of the labeled data while the unsupervised term provides effective regularization to model local relationships of the unlabeled data. We apply an efficient sequential procedure to learn the hashing functions. Experimental comparisons with other state-of-the-art methods on three large scale datasets demonstrate the effectiveness and efficiency of our method.

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          cover image ACM Conferences
          MM '14: Proceedings of the 22nd ACM international conference on Multimedia
          November 2014
          1310 pages
          ISBN:9781450330633
          DOI:10.1145/2647868

          Copyright © 2014 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 3 November 2014

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          MM '14 Paper Acceptance Rate55of286submissions,19%Overall Acceptance Rate995of4,171submissions,24%

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