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2017 IEEE International Conference on Multimedia and Expo (ICME) (2017)
Hong Kong, Hong Kong
July 10, 2017 to July 14, 2017
ISSN: 1945-788X
ISBN: 978-1-5090-6068-9
pp: 127-132
Xin Gao , Center for Future Media & School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
Fumin Shen , Center for Future Media & School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
Yang Yang , Center for Future Media & School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
Xing Xu , Center for Future Media & School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
Hanxi Li , JiangXi Normal University, China
Heng Tao Shen , Center for Future Media & School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
ABSTRACT
Learning based hashing has become increasingly popular because of its high efficiency in handling the large scale image retrieval. Preserving the pairwise similarities of data points in the Hamming space is critical in state-of-the-art hashing techniques. However, most previous methods ignore to capture the local geometric structure residing on original data, which is essential for similarity search. In this paper, we propose a novel hashing framework, which simultaneously optimizes similarity preserving hash codes and reconstructs the locally linear structures of data in the Hamming space. In specific, we learn two hash functions such that the resulting two sets of binary codes can well preserve the pairwise similarity and sparse neighborhood in the original feature space. By taking advantage of the flexibility of asymmetric hash functions, we devise an efficient alternating algorithm to optimize the hash coding function and high-quality binary codes jointly. We evaluate the proposed method on several large-scale image datasets, and the results demonstrate it significantly outperforms recent state-of-the-art hashing methods on large-scale image retrieval problems.
INDEX TERMS
Binary codes, Encoding, Optimization, Manifolds, Quantization (signal), Image reconstruction, Semantics
CITATION

X. Gao, F. Shen, Y. Yang, X. Xu, H. Li and H. T. Shen, "Asymmetric sparse hashing," 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, Hong Kong, 2017, pp. 127-132.
doi:10.1109/ICME.2017.8019306
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