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Robust Supervised Hashing

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Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 662))

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Abstract

Hashing methods on large scale image retrieval have been extensively in attention. These methods can be roughly categorized as supervised and unsupervised. Unsupervised hashing methods mainly search for a projection matrix of the original data to preserve the Euclidean distance similarity, while supervised hashing methods aim to preserve the label similarity. However, most hashing methods propose a complicated objective function and search for optimized or relaxed solutions. Some methods will consume much time to train a good binary code. This paper is not focusing on formulating a complex solution like the previous state-of-art methods. Contrarily, we firstly propose a simple objective function on supervised hashing as far as we have learned. And we devise a novel solution which uses a maximum and equal Hamming distance code to construct the label information. This method keeps a comparable accuracy with the state-of-the-art supervised hashing methods.

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Notes

  1. 1.

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  2. 2.

    http://vision.princeton.edu/projects/2010/SUN/. Accessed 20 May 2016.

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Acknowledgments

This work was partially sponsored by supported by the NSFC (National Natural Science Foundation of China) under Grant No. 61375031, No. 61573068, No. 61471048, and No.61273217, the Fundamental Research Funds for the Central Universities under Grant No. 2014ZD03-01, This work was also supported by Beijing Nova Program, CCF-Tencent Open Research Fund, and the Program for New Century Excellent Talents in University.

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Tongtong Yuan & Weihong Deng

Authors
  1. Tongtong Yuan
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  2. Weihong Deng
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Corresponding author

Correspondence to Weihong Deng .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Yuan, T., Deng, W. (2016). Robust Supervised Hashing. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_47

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  • DOI: https://doi.org/10.1007/978-981-10-3002-4_47

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3001-7

  • Online ISBN: 978-981-10-3002-4

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