Abstract
Hashing methods are becoming increasingly popular because they can achieve fast retrieval of large-scale data by representing the images with binary codes. However, the traditional hashing methods tend to obtain the binary codes by relaxing the discrete problems which greatly increase the information loss. In this paper, we propose a novel hash learning method, called Supervised Locality Preserving Hashing (SLPH) for image retrieval. Different from the traditional two-steps methods which learn low-dimensional features and binary codes of the data separately, we directly obtain the binary codes and thus reduce the information loss. Besides, we add graph-regularized learning on the designed model to avoid over-fitting and improve the performance. Experiments on two benchmark databases show that the proposed SLPH performs better than some state-of-the-art methods.
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Acknowledgements
This work was supported in part by the Natural Science Foundation of China (Grant 61573248, Grant 61773328, Grant 61732011 and Grant 61703283), Research Grant of The Hong Kong Polytechnic University (Project Code: G-UA2B), China Postdoctoral Science Foundation (Project 2016M590812 and Project 2017T100645), the Guangdong Natural Science Foundation (Project 2017A030313367 and Project 2017A030310067), and Shenzhen Municipal Science and Technology Innovation Council (No. JCYJ20170302153434048 and No. JCYJ20160429182058044).
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Zhou, X., Lai, Z., Chen, Y. (2019). Supervised Locality Preserving Hashing. In: Wong, W. (eds) Artificial Intelligence on Fashion and Textiles. AITA 2018. Advances in Intelligent Systems and Computing, vol 849. Springer, Cham. https://doi.org/10.1007/978-3-319-99695-0_24
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DOI: https://doi.org/10.1007/978-3-319-99695-0_24
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