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Maximum margin hashing with supervised information

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Abstract

Binary code is a kind of special representation of data. With the binary format, hashing framework can be built and a large amount of data can be indexed to achieve fast research and retrieval. Many supervised hashing approaches learn hash functions from data with supervised information to retrieve semantically similar samples. This kind of supervised information can be generated from external data other than pixels. Conventional supervised hashing methods assume a fixed relationship between the Hamming distance and the similar (dissimilar) labels. This assumption leads to too rigid requirement in learning and makes the similar and dissimilar pairs not distinguishable. In this paper, we adopt a large margin principle and define a Hamming margin to formulate such relationship. At the same time, inspired by support vector machine which achieves strong generalization capability by maximizing the margin of its decision surface, we propose a binary hash function in the same manner. A loss function is constructed corresponding to these two kinds of margins and is minimized by a block coordinate descent method. The experiments show that our method can achieve better performance than the state-of-the-art hashing methods.

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Notes

  1. http://yann.lecun.com/exdb/mnist/

  2. http://www.svcl.ucsd.edu/projects/crossmodal/

References

  1. Bai X, Yang H, Zhou J, Ren P, Cheng J (2014) Data-dependent hashing based on p-stable distribution. IEEE Trans Image Process 23(12):5033–5046

    Article  MathSciNet  Google Scholar 

  2. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  3. Bronstein MM, Bronstein AM, Michel F, Paragios N (2010) Data fusion through cross-modality metric learning using similarity-sensitive hashing. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3594–3601

  4. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27

    Article  Google Scholar 

  5. Charikar MS (2002) Similarity estimation techniques from rounding algorithms. In: Proceedings of the 34th annual ACM symposium on theory of computing. ACM, pp 380–388

  6. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  7. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1): 21–27

    Article  MATH  Google Scholar 

  8. Datar M, Immorlica N, Indyk P, Mirrokni V (2004) Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the annual symposium on computational geometry, pp 253–262

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

    Article  Google Scholar 

  10. He K, Wen F, Sun J (2013) K-means hashing: an affinity-preserving quantization method for learning binary compact codes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2938–2945

  11. Heo JP, Lee Y, He J, Chang SF, Yoon SE (2012) Spherical hashing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2957–2964

  12. Indyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the annual ACM symposium on theory of computing, pp 604–613

  13. Jegou H, Douze M, Schmid C (2011) Product quantization for nearest neighbor search. IEEE Trans Pattern Anal Mach Intell 33(1):117–128

    Article  Google Scholar 

  14. Jiang YG, Wang J, Xue X, Chang SF (2013) Query-adaptive image search with hash codes. IEEE Trans Multimedia 15(2):442–453

    Article  Google Scholar 

  15. Kong W, Li WJ (2012) Isotropic hashing. In: Proceedings of the neural information processing systems conference, pp 1655–1663

  16. Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Master’s thesis, Department of Computer Science, University of Toronto

  17. Kulis B, Darrell T (2009) Learning to hash with binary reconstructive embeddings. In: Proceedings of the neural information processing systems conference, vol 22, pp 1042–1050

  18. Kulis B, Grauman K (2012) Kernelized locality-sensitive hashing. IEEE Trans Pattern Anal Mach Intell 34(6):1092–1104

    Article  Google Scholar 

  19. Kulis B, Jain P, Grauman K (2009) Fast similarity search for learned metrics. IEEE Trans Pattern Anal Mach Intell 31(12):2143–2157

    Article  Google Scholar 

  20. Kumar S, Udupa R (2011) Learning hash functions for cross-view similarity search. In: IJCAI proceedings-international joint conference on artificial intelligence, vol 22, p 1360

  21. Leng C, Wu J, Cheng J, Bai X, Lu H (2015) Online sketching hashing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2503–2511

  22. Li P, Wang M, Cheng J, Xu C, Lu H (2013) Spectral hashing with semantically consistent graph for image indexing. IEEE Trans Multimedia 15(1):141–152

    Article  Google Scholar 

  23. Liu W, Wang J, Ji R, Jiang Y, Chang S (2012) Supervised hashing with kernels. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2074–2081

  24. Liu X, Du B, Deng C, Liu M, Lang B (2015) Structure sensitive hashing with adaptive product quantization. IEEE Transactions on Cybernetics PP(0), pp 1–12

  25. Liu X, He J, Lang B (2014) Multiple feature kernel hashing for large-scale visual search. Pattern Recogn 47(2):748–757

    Article  MATH  Google Scholar 

  26. Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  27. Mu Y, Shen J, Yan S (2010) Weakly-supervised hashing in kernel space. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3344–3351

  28. Norouzi M, Fleet DJ (2011) Minimal loss hashing for compact binary codes. In: Proceedings of the international conference on machine learning, pp 353–360

  29. Norouzi M, Fleet DJ, Salakhutdinov R (2012) Hamming distance metric learning. In: Proceedings of the neural information processing systems conference, pp 1070–1078

  30. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175

    Article  MATH  Google Scholar 

  31. Olsson C, Eriksson AP, Kahl F (2007) Solving large scale binary quadratic problems: spectral methods vs. semidefinite programming. In: Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, pp 1–8

  32. Salakhutdinov R, Hinton G (2009) Semantic hashing. Int J Approx Reason 50(7):969–978

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Wang J, Kumar S, Chang SF (2010) Sequential projection learning for hashing with compact codes. In: Proceedings of the international conference on machine learning, pp 1127–1134

  35. Weiss Y, Fergus R, Torralba A (2012) Multidimensional spectral hashing. In: Proceedings of the european conference on computer vision, pp 340–353

  36. Weiss Y, Torralba A, Fergus R (2008) Spectral hashing. In: Proceedings of the neural information processing systems conference, pp 1753–1760

  37. Xia R, Pan Y, Lai H, Liu C, Yan S (2014) Supervised hashing for image retrieval via image representation learning. In: 28th AAAI conference on artificial intelligence

  38. Yan L, Ling H, Ye D, Wang C, Ye Z, Chen H (2015) Feature fusion based hashing for large scale image copy detection. Int J Comput Int Sys 8(4):725–734

    Article  Google Scholar 

  39. Yan L, Zou F, Guo R, Gao L, Zhou K, Wang C (2015) Feature aggregating hashing for image copy detection. World Wide Web, pp 1–13

  40. Yang H, Bai X, Liu C, Zhou J (2013) Label propagation hashing based on p-stable distribution and coordinate descent. In: Proceedings of the international conference on image processing

  41. Yang H, Bai X, Zhou J, Ren P, Zhang Z, Cheng J (2014) Adaptive object retrieval with kernel reconstructive hashing. In: 2014 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1955–1962

  42. Zhang D, Li W J (2014) Large-scale supervised multimodal hashing with semantic correlation maximization. In: Proceedings of the AAAI conference on artificial intelligence

  43. Zhang D, Wang J, Cai D, Lu J (2010) Self-taught hashing for fast similarity search. In: Proceedings of the international ACM SIGIR conference on research and development in information retrieval , pp 18–25

  44. Zhang H, Bai X, Zhou J, Cheng J, Zhao H (2013) Object detection via structural feature selection and shape model. IEEE Trans Image Process 22(12):4984–4995

    Article  MathSciNet  Google Scholar 

  45. Zhang T, Du C, Wang J (2014) Composite quantization for approximate nearest neighbor search. In: Proceedings of the 31st international conference on machine learning (ICML-14), pp 838–846

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Acknowledgments

This work was supported by NSFC projects (No. 61370123 and 61503422), and the Australian Research Councils DECRA Projects funding scheme (project ID 522 DE120102948).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, China

    Haichuan Yang, Xiao Bai, Yanzhen Liu & Wenzhong Tang

  2. School of Aeronautic Science and Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, China

    Yanyang Wang

  3. School of Information, Central University of Finance and Economics, 100081, Beijing, China

    Lu Bai

  4. School of Information and Communication Technology, Griffith University, Nathan, QLD 4111, Australia

    Jun Zhou

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  1. Haichuan Yang
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  2. Xiao Bai
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  5. Lu Bai
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  6. Jun Zhou
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Correspondence to Xiao Bai.

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Yang, H., Bai, X., Liu, Y. et al. Maximum margin hashing with supervised information. Multimed Tools Appl 75, 3955–3971 (2016). https://doi.org/10.1007/s11042-015-3159-3

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  • DOI: https://doi.org/10.1007/s11042-015-3159-3

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