Unsupervised discriminative hashing |
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| Affiliation: | 1. School of Information Science and Engineering, Lanzhou University, Lanzhou, China;2. School of Art and Design, Zhejiang Sci-Tech University, Hangzhou, China;1. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. University Multimedia, Malaysia;3. The Chinese University of Hong Kong, Hong Kong;1. Department of Computer Science and Engineering, Jatiya Kabi Kazi Nazrul Islam University, Bangladesh;2. Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Bangladesh;1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Information, Qilu University of Technology, Jinan 250353, China |
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| Abstract: | Hashing is one of the popular solutions for approximate nearest neighbor search because of its low storage cost and fast retrieval speed, and many machine learning algorithms are adapted to learn effective hash function. As hash codes of the same cluster are similar to each other while the hash codes in different clusters are dissimilar, we propose an unsupervised discriminative hashing learning method (UDH) to improve discrimination among hash codes in different clusters. UDH shares a similar objective function with spectral hashing algorithm, and uses a modified graph Laplacian matrix to exploit local discriminant information. In addition, UDH is designed to enable efficient out-of-sample extension. Experiments on real world image datasets demonstrate the effectiveness of our novel approach for image retrieval. |
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| Keywords: | Unsupervised discriminative hashing Out-of-sample extrapolation Manifold learning |
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