Abstract
This paper proposes a double-coding density sensitive hashing (DCDSH) method. DCDSH accomplishes approximate nearest neighbor (ANN) search tasks based on its double coding scheme. First, DCDSH generates real-valued hash codes by projecting objects along the principle hyper-planes. These hyper-planes are determined by principle distributions and geometric structures of data set. Second, DCDSH derives binary hash codes based on these real-valued hash codes. Real-valued hash codes can avoid undesirable partition of objects in low density areas and effectively improve representation capability and discriminating power. Binary codes contribute to query speed owing to the low complexity for computing hamming distance. DCDSH integrates the advantages of these two kinds of hash codes. Experimental results on large scale high dimensional data show that the proposed DCDSH exhibits superior performance compared to several state-of-the-art hashing methods.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (61401185, 61601213, and 61402212), China Postdoctoral Science Foundation Funded Project (2017M611252, 2016M591452), Natural Science Foundation of Liaoning General Project (LJYL017, LJYL018), Natural Science Foundation of Liaoning Province (2015020098), and Program for Liaoning Excellent Talents in University (LJQ2015045). All of these supports are appreciated. We would also thank the anonymous referees for their detailed comments and suggestions.
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Tang, X., Wang, X., Jia, D., Song, W., Meng, X. (2017). Double-Coding Density Sensitive Hashing. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_45
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DOI: https://doi.org/10.1007/978-3-319-70093-9_45
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