Abstract
Hashing methods play an important role in large-scale image retrieval, and have been widely applied due to fast approximate nearest neighbor search and efficient data storage. However, most existing hashing methods are not taken the low-dimensional manifold into account in nearest neighbors search. In this paper, we propose an effective hashing method to preserve the intrinsic structure of high-dimensional data points in the low-dimensional manifold space. In particular, we introduce a compressed algorithm to learn a smaller synthetic data set represent the database in the original space so that the approximated nearest neighbors can be quickly discovered. To this end, we exploit the manifold learning generate appropriate binary code. Experimental results on benchmark data sets show that the proposed approach is effective in comparison with state-of-the-art methods.
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Acknowledgement
This research was supported by the National Natural Science Foundation of China (61472304, 61125204, 61432014 and 61201294), the Fundamental Research Funds for the Central Universities (Grant No. K5051202048, BDZ021403, JB149901), Program for Changjiang Scholars and Innovative Research Team in University (No. IRT13088).
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Ding, L., Wang, X., Gao, X. (2015). Compressed Hashing for Neighborhood Structure Preserving. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_25
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DOI: https://doi.org/10.1007/978-3-319-23989-7_25
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