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Optimized high order product quantization for approximate nearest neighbors search

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Abstract

Product quantization is now considered as an effective approach to solve the approximate nearest neighbor (ANN) search. A collection of derivative algorithms have been developed. However, the current techniques ignore the intrinsic high order structures of data, which usually contain helpful information for improving the computational precision. In this paper, aiming at the complex structure of high order data, we design an optimized technique, called optimized high order product quantization (O-HOPQ) for ANN search. In O-HOPQ, we incorporate the high order structures of the data into the process of designing a more effective subspace decomposition way. As a result, spatial adjacent elements in the high order data space are grouped into the same sub-space. Then, O-HOPQ generates its spatial structured code-book, by optimizing the quantization distortion. Starting from the structured codebook, the global optimum quantizers can be obtained effectively and efficiently. Experimental results show that appropriate utilization of the potential information that exists in the complex structure of high order data will result in significant improvements to the performance of the product quantizers. Besides, the high order structure based approaches are effective to the scenario where the data have intrinsic complex structures.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61732011) and Applied Fundamental Research Program of Qinghai Province (2019-ZJ-7017).

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Authors and Affiliations

  1. School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China

    Linhao Li

  2. Tianjin Key Lab of Machine Learning, School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Linhao Li & Qinghua Hu

Authors
  1. Linhao Li
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  2. Qinghua Hu
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Corresponding author

Correspondence to Linhao Li.

Additional information

Linhao Li received his BS degree in Applied Mathematics from Tianjin University, China in 2012, his MS degree in Computational Mathematics from Tianjin University, China in 2014 his PhD degree in Computer Science from Tianjin University, China in 2019. Now he is working as a assistant professor in School of Artificial Intelligence, Hebei University of Technology, China. His research interests focus on quantization and hashing learning, sparse signal recovery, background modeling and foreground detection.

Qinghua Hu received his BS, MS and PhD degrees from Harbin Institute of Technology, China in 1999, 2002 and 2008, respectively. He worked as a postdoctoral fellow with Department of Computing, Hong Kong Polytechnic University, China from 2009 to 2011.

Now he is Dean of School of Artificial Intelligence, the Vice Chairman of Tianjin Branch of China Computer Federation, the Vice Directior of SIG Granular Computing and Knowledge Discovery, Chinese Association of Artificial Intelligence, China. He is Associate Editor of IEEE Transactions on Fuzzy Systems, ACTA AUTOMATICA SINICA and Engergies. He now is supported by Key Program, National Natural Science Foundation of China. His research focuses on uncertainty modeling in big data, machine learning with multi-modality data, intelligent unmanned systems. He has published more than 200 peer-reviewed papers.

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Li, L., Hu, Q. Optimized high order product quantization for approximate nearest neighbors search. Front. Comput. Sci. 14, 259–272 (2020). https://doi.org/10.1007/s11704-018-7049-5

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  • DOI: https://doi.org/10.1007/s11704-018-7049-5

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