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International Computer Symposium

ICS 2018: New Trends in Computer Technologies and Applications pp 141–149Cite as

Supervised Representation Hash Codes Learning

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1013))

Abstract

Learning-based hashing has been widely employed for large-scale similarity retrieval due to its efficient computation and compressed storage. In this paper, we propose ResHash, a deep representation hash code learning approach to learning compact and discriminative binary codes. In ResHash, we assume that each semantic label has its own representation codeword and these codewords guide hash coding. The codewords are attractors that attract semantically similar images and are also repulsors that repel semantically dissimilar ones. Furthermore, ResHash jointly learns compact binary codes and discover representation codewords from data by a simple margin ranking loss, making it easily realizable and avoiding the need to hand-craft the codewords beforehand. Experimental results on standard benchmark datasets show the effectiveness of ResHash.

This work is supported in part by the Ministry of Science and Technology of Taiwan under contract MOST 107-2634-F-001-004 and MOST 107-2218-E-390-006-MY2.

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References

  1. Bojanowski, P., Joulin, A., Lopez-Paz, D., Szlam, A.: Optimizing the latent space of generative networks. CoRR abs/1707.05776 (2017)

    Google Scholar 

  2. Cao, Y., Long, M., Wang, J., Liu, S.: Deep visual-semantic quantization for efficient image retrieval. In: CVPR (2017)

    Google Scholar 

  3. Cao, Z., Long, M., Wang, J., Yu, P.S.: HashNet: deep learning to hash by continuation. In: ICCV (2017)

    Google Scholar 

  4. Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: BMVC (2014)

    Google Scholar 

  5. Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: CVPR, pp. 2414–2423 (2016)

    Google Scholar 

  6. Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916–2929 (2013)

    Article  Google Scholar 

  7. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)

    Google Scholar 

  8. Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: NIPS, pp. 1042–1050 (2009)

    Google Scholar 

  9. Lai, H., Pan, Y., Liu, Y., Yan, S.: Simultaneous feature learning and hash coding with deep neural networks. In: CVPR, pp. 3270–3278 (2015)

    Google Scholar 

  10. Li, W., Wang, S., Kang, W.: Feature learning based deep supervised hashing with pairwise labels. In: IJCAI, pp. 1711–1717 (2016)

    Google Scholar 

  11. Lin, K., Yang, H.F., Hsiao, J.H., Chen, C.S.: Deep learning of binary hash codes for fast image retrieval. In: CVPRW on Deep Vision, pp. 27–35 (2015)

    Google Scholar 

  12. Norouzi, M., Fleet, D.J.: Minimal loss hashing for compact binary codes. In: ICML, pp. 353–360 (2011)

    Google Scholar 

  13. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  14. Shen, F., Shen, C., Liu, W., Shen, H.T.: Supervised discrete hashing. In: CVPR, pp. 37–45 (2015)

    Google Scholar 

  15. Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. CoRR abs/1312.6034 (2013)

    Google Scholar 

  16. Wang, J., Kumar, S., Chang, S.: Semi-supervised hashing for large-scale search. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2393–2406 (2012)

    Article  Google Scholar 

  17. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NIPS, pp. 1753–1760 (2008)

    Google Scholar 

  18. Xia, R., Pan, Y., Lai, H., Liu, C., Yan, S.: Supervised hashing for image retreieval via image representation learning. In: AAAI, pp. 2156–2162 (2014)

    Google Scholar 

  19. Yang, H.F., Lin, K., Chen, C.S.: Supervised learning of semantics-preserving hash via deep convolutional neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 437–451 (2018)

    Article  Google Scholar 

  20. Zhang, R., Lin, L., Zhang, R., Zuo, W., Zhang, L.: Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans. Image Process. 24(12), 4766–4779 (2015)

    Article  MathSciNet  Google Scholar 

  21. Zhao, F., Huang, Y., Wang, L., Tan, T.: Deep semantic ranking based hashash for multi-label image retreieval. In: CVPR, pp. 1556–1564 (2015)

    Google Scholar 

  22. Zhu, H., Long, M., Wang, J., Cao, Y.: Deep hashing network for efficient similarity retrieval. In: AAAI, pp. 2415–2421 (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Huei-Fang Yang

  2. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Cheng-Hao Tu & Chu-Song Chen

  3. MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan

    Chu-Song Chen

Authors
  1. Huei-Fang Yang
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  2. Cheng-Hao Tu
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  3. Chu-Song Chen
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Corresponding author

Correspondence to Huei-Fang Yang .

Editor information

Editors and Affiliations

  1. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chuan-Yu Chang

  2. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chien-Chou Lin

  3. Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Horng-Horng Lin

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Cite this paper

Yang, HF., Tu, CH., Chen, CS. (2019). Supervised Representation Hash Codes Learning. In: Chang, CY., Lin, CC., Lin, HH. (eds) New Trends in Computer Technologies and Applications. ICS 2018. Communications in Computer and Information Science, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-13-9190-3_13

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  • DOI: https://doi.org/10.1007/978-981-13-9190-3_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9189-7

  • Online ISBN: 978-981-13-9190-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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