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.
- C. M. Bishop et al. Pattern recognition and machine learning, volume 1. springer New York, 2006. Google ScholarDigital Library
- R. O. Duda, P. E. Hart, and D. G. Stork. Pattern classification. John Wiley & Sons, 2012.Google ScholarDigital Library
- K. Fukunaga. Introduction to statistical pattern recognition. Academic press, 1990. Google ScholarDigital Library
- A. Gionis, P. Indyk, R. Motwani, et al. Similarity search in high dimensions via hashing. In VLDB, pages 518--529, 1999. Google ScholarDigital Library
- X. He and P. Niyogi. Locality preserving projections. In NIPS, 2003.Google ScholarDigital Library
- G. Irie, Z. Li, X.-M. Wu, and S.-F. Chang. Locally linear hashing for extracting non-linear manifolds. In CVPR, 2014. Google ScholarDigital Library
- S. Kim, Y. Kang, and S. Choi. Sequential spectral learning to hash with multiple representations. In ECCV, pages 538--551. Springer-Verlag, 2012. Google ScholarDigital Library
- B. Kulis and T. Darrell. Learning to hash with binary reconstructive embeddings. In NIPS, volume 22, pages 1042--1050, 2009.Google Scholar
- A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. International journal of computer vision, 42(3):145--175, 2001. Google ScholarDigital Library
- C. Strecha, A. M. Bronstein, M. M. Bronstein, and P. Fua. Ldahash: Improved matching with smaller descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(1):66--78, 2012. Google ScholarDigital Library
- J. Wang, S. Kumar, and S.-F. Chang. Semi-supervised hashing for large-scale search. IEEE Trans. on Pattern Analysis and Machine Intelligence, 34(12):2393--2406, 2012. Google ScholarDigital Library
- Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, pages 1753--1760, 2008.Google ScholarDigital Library
Index Terms
Locality Preserving Discriminative Hashing
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