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Discrete Multi-graph Hashing for Large-Scale Visual Search

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Abstract

Hashing has become a promising technique to be applied to the large-scale visual retrieval tasks. Multi-view data has multiple views, providing more comprehensive information. The challenges of using hashing to handle multi-view data lie in two aspects: (1) How to integrate multiple views effectively? (2) How to reduce the distortion error in the quantization stage? In this paper, we propose a novel hashing method, called discrete multi-graph hashing (DMGH), to address the above challenges. DMGH uses a multi-graph learning technique to fuse multiple views, and adaptively learns the weights of each view. In addition, DMGH explicitly minimizes the distortion errors by carefully designing a quantization regularization term. An alternative algorithm is developed to solve the proposed optimization problem. The optimization algorithm is very efficient due to the low-rank property of the anchor graph. The experiments on three large-scale datasets demonstrate the proposed method outperforms the existing multi-view hashing methods.

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Notes

  1. http://www.cs.toronto.edu/~kriz/cifar.html.

  2. http://authors.library.caltech.edu/7694/.

  3. http://lms.comp.nus.edu.sg/research/NUS-WIDE.htm.

References

  1. Bertsekas PD (1999) Nonlinear programming. Athena Scientific, Belmont

    MATH  Google Scholar 

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

  3. Chen X, Cai D (2011) Large scale spectral clustering with landmark-based representation. In: Proceedings of AAAI conference on artificial intelligence

  4. Chua TS, Tang J, Hong R, Li H, Luo Z, Zheng Y (2009) NUS-WIDE: a real-world web image database from national University of Singapore. In: Proceedings of ACM international conference on image and video retrieval, pp 1–9

  5. Ding G, Guo Y, Zhou J (2014) Collective matrix factorization hashing for multimodal data. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2083–2090

  6. Gionis A, Indyk P, Motwani R (1999) Similarity search in high dimensions via hashing. In: Proceedings of international conference on very large data bases, pp 518–529

  7. 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 

  8. Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset. Technical report

  9. Kim S, Kang Y, Choi S (2012) Sequential spectral learning to hash with multiple representations. In: Proceedings of European conference on computer vision, pp 538–551

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

  11. Kumar S, Udupa R (2011) Learning hash functions for cross-view similarity search. In: Proceedings of international joint conference on artificial intelligence, pp 1360–1365

  12. Liu L, Yu M, Shao L (2015) Multiview alignment hashing for efficient image search. IEEE Trans Image Process 24(3):956–966

    Article  MathSciNet  MATH  Google Scholar 

  13. Liu W, Tsang IW (2016) Sparse perceptron decision tree for millions of dimensions. In: Proceedings of AAAI conference on artificial intelligence, pp 1881–1887

  14. Liu W, Tsang IW (2017) Making decision trees feasible in ultrahigh feature and label dimensions. J Mach Learn Res 18(81):1–36

    MathSciNet  MATH  Google Scholar 

  15. Liu W, He J, Chang S (2010) Large graph construction for scalable semi-supervised learning. In: Proceedings of international conference on machine learning, pp 679–686

  16. Liu W, Wang J, Kumar S, Chang SF (2011) Hashing with graphs. In: Proceedings of international conference on machine learning, pp 1–8

  17. Liu W, Mu C, Kumar S, Chang SF (2014) Discrete graph hashing. In: Proceedings of advances in neural information processing systems, pp 3419–3427

  18. Liu X, He J, Liu D, Lang B (2012) Compact kernel hashing with multiple features. In: Proceedings of ACM international conference on multimedia, pp 881–884

  19. Luo Y, Tao D, Ramamohanarao K, Xu C, Wen Y (2015) Tensor canonical correlation analysis for multi-view dimension reduction. IEEE Trans Knowl Data Eng 27(11):3111–3124

    Article  Google Scholar 

  20. Luo Y, Wen Y, Tao D, Gui J, Xu C (2016) Large margin multi-modal multi-task feature extraction for image classification. IEEE Trans Image Process 25(1):414–427

    Article  MathSciNet  MATH  Google Scholar 

  21. Schönemann PH (1966) A generalized solution of the orthogonal procrustes problem. Psychometrika 31(1):1–10

    Article  MathSciNet  MATH  Google Scholar 

  22. Shen F, Shen C, Liu W, Tao Shen H (2015a) Supervised discrete hashing. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 37–45

  23. Shen F, Zhou X, Yang Y, Song J, Shen HT, Tao D (2016) A fast optimization method for general binary code learning. IEEE Trans Image Process 25(12):5610–5621

    Article  MathSciNet  MATH  Google Scholar 

  24. Shen F, Yang Y, Liu L, Liu W, Tao D, Shen HT (2017a) Asymmetric binary coding for image search. IEEE Trans Multimed 19(9):2022–2032

    Article  Google Scholar 

  25. Shen F, Xu Y, Liu L, Yang Y, Huang Z, Tao SH (2018a) Unsupervised deep hashing with similarity-adaptive and discrete optimization. IEEE Trans Pattern Anal Mach Intell 1:1–1

    Google Scholar 

  26. Shen X, Shen F, Sun QS, Yuan YH (2015b) Multi-view latent hashing for efficient multimedia search. In: Proceedings of ACM international conference on multimedia, pp 831–834

  27. Shen X, Shen F, Sun QS, Yang Y, Yuan YH, Shen HT (2017b) Semi-paired discrete hashing: learning latent hash codes for semi-paired cross-view retrieval. IEEE Trans Cybern 47(12):1–14

    Article  Google Scholar 

  28. Shen X, Liu W, Tsang IW, Sun QS, Ong YS (2018b) Multilabel prediction via cross-view search. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2017.2763967

  29. Shen X, Shen F, Liu L, Yuan YH, Liu W, Sun QS (2018c) Multiview discrete hashing for scalable multimedia search. ACM Trans Intell Syst Technol 9(5):53:1–53:21

    Article  Google Scholar 

  30. Song J, Yang Y, Huang Z, Shen HT, Hong R (2011) Multiple feature hashing for real-time large scale near-duplicate video retrieval. In: Proceedings of ACM international conference on multimedia, pp 423–432

  31. Song J, Yang Y, Yang Y, Huang Z, Shen HT (2013) Inter-media hashing for large-scale retrieval from heterogeneous data sources. In: Proceedings of ACM SIGMOD international conference on management of data, pp 785–796

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

    Article  Google Scholar 

  33. Wang J, Shen HT, Song J, Ji J (2014) Hashing for similarity search: a survey. arXiv preprint arXiv:1408.2927

  34. Weiss Y, Torralba A, Fergus R (2009) Spectral hashing. In: Proceedings of advances in neural information processing systems, pp 1753–1760

  35. Xu C, Tao D, Xu C (2013) A survey on multi-view learning. arXiv:1304.5634 [CoRR]

  36. Zhang D, Wang F, Si L (2011) Composite hashing with multiple information sources. In: Proceedings of ACM SIGIR conference on research and development in information retrieval, pp 225–234

  37. Zhu X, Huang Z, Shen HT, Zhao X (2013) Linear cross-modal hashing for efficient multimedia search. In: Proceedings of ACM international conference on multimedia, pp 143–152

Download references

Acknowledgements

This project is supported by National Natural Science Foundation of China (Nos. 61202439 and 61772561), and partly supported by Scientific Research Fundation of Hunan Provincial Education Department of China (No. 16A008) and Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems (No. 2017TP1016).

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Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, 410114, China

    Lingyun Xiang

  2. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

    Lingyun Xiang

  3. Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha, 410114, China

    Lingyun Xiang

  4. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xiaobo Shen

  5. College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China

    Jiaohua Qin

  6. School of Traffic & Transportation Engineering, Changsha University of Science and Technology, Changsha, 410114, China

    Wei Hao

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  1. Lingyun Xiang
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  2. Xiaobo Shen
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  3. Jiaohua Qin
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  4. Wei Hao
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Correspondence to Xiaobo Shen.

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Xiang, L., Shen, X., Qin, J. et al. Discrete Multi-graph Hashing for Large-Scale Visual Search. Neural Process Lett 49, 1055–1069 (2019). https://doi.org/10.1007/s11063-018-9892-7

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  • DOI: https://doi.org/10.1007/s11063-018-9892-7

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