Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Concentrated hashing with neighborhood embedding for image retrieval and classification

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Hashing learning is efficient for large-scale image retrieval by using the nearest neighbor search with binary codes instead of continuous representations. With the success of deep neural networks in related tasks such as data representation, recent hashing methods based on deep learning can further improve image retrieval quality and classification accuracy. However, most existing methods are primarily designed to maximize the performance of retrieval based on linear scan of hash codes which is still time-consuming on large-scale datasets. Fortunately, Hamming space retrieval is an alternative as it is less time-consuming by retrieving data points that are within a Hamming ball with a given Hamming radius, but few works focus on that. In this paper, we propose a concentrated hashing method with neighborhood embedding (CHNE) for efficient and effective image retrieval and classification. By integrating Cauchy cross-entropy and pair-wise weighted similarity loss, CHNE can enable similar data pairs with smaller Hamming distance and dissimilar data pairs with larger Hamming distance. In addition, existing hashing methods are usually designed for retrieval, thus the performance of classification using the binary codes is not guaranteed. To tackle this problem, we jointly minimize the regression quantization and neighborhood structure reconstruction errors in the loss function to improve the classification accuracy. The proposed end-to-end deep hashing method can be optimized by back-propagation in a standard manner. Experimental results on several datasets demonstrate that the proposed method can improve the performance of retrieval and classification. Due to its generality, the proposed method is expected to be useful for image retrieval and classification in broader areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Wang J, Zhang T, Sebe N, Shen HT et al (2017) A survey on learning to hash. IEEE Trans Pattern Anal Mach Intell 40(4):769–790

    Article  Google Scholar 

  2. Di H, Nie F, Li X (2018) Deep binary reconstruction for cross-modal hashing. IEEE Trans Multimed 21(4):973–985

    Google Scholar 

  3. Kuang Z, Zhang X, Jun Y, Li Z, Fan J (2021) Deep embedding of concept ontology for hierarchical fashion recognition. Neurocomputing 425:191–206

    Article  Google Scholar 

  4. Brasnett P, Bober M (2008) Fast and robust image identification. In: 2008 19th International Conference on pattern recognition, pp 1–5. IEEE, 2008

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

  6. Torralba A, Fergus R, Weiss Y, et al (2008) Small codes and large image databases for recognition. In: CVPR, volume 1, page 2. Citeseer, 2008

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

    Article  Google Scholar 

  8. Andoni A, Indyk P (2006) Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: 2006 47th Annual IEEE Symposium on foundations of computer science (FOCS’06), pp. 459–468. IEEE, 2006

  9. Gong Y, Lazebnik S, Gordo A, Perronnin F (2012) 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. Strecha C, Bronstein A, Bronstein M, Fua P (2011) Ldahash: improved matching with smaller descriptors. IEEE Trans Pattern Anal Mach Intell 34(1):66–78

    Article  Google Scholar 

  11. Kulis B, Darrell T (2009) Learning to hash with binary reconstructive embeddings. In: Bengio Y, Schuurmans D, Lafferty J, Williams C, Culotta A (eds) Advances in neural information processing systems, vol 2. pp 1042–1050

  12. Liu W, Wang J, Ji R, Jiang Y-G, Chang S-F (2012) Supervised hashing with kernels. In: 2012 IEEE Conference on computer vision and pattern recognition, pp 2074–2081. IEEE, 2012

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

  14. Shen F, Shen C, Shi Q, Van Den Hengel A, Tang Z (2013) Inductive hashing on manifolds. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 1562–1569, 2013

  15. Liu Y, Feng L, Liu S, Sun M (2019) An elm based local topology preserving hashing. Int J Mach Learn Cybern 10(10):2691–2708

    Article  Google Scholar 

  16. Cao Z, Long M, Wang J, Yu PS (2017) Hashnet: deep learning to hash by continuation. In: Proceedings of the IEEE International Conference on computer vision, pp 5608–5617, 2017

  17. Zhu H, Long M, Wang J, Cao Y (2016) Deep hashing network for efficient similarity retrieval. In: Proceedings of the thirtieth AAAI conference on artificial intelligence (AAAI-16), vol 30(1). pp 2415–2421

  18. Zhou X, Shen F, Liu L, Liu W, Nie L, Yang Y, Shen H-T (2018) Graph convolutional network hashing. IEEE Trans Cybern 50(4):1460–1472

    Article  Google Scholar 

  19. Li J, Ng WWY, Tian X, Kwong S, Wang H (2020) Weighted multi-deep ranking supervised hashing for efficient image retrieval. Int J Mach Learn Cybern 11(4):883–897

    Article  Google Scholar 

  20. van der Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

  21. Cakir F, He K, Adel Bargal S, Sclaroff S (2017) Mihash: online hashing with mutual information. In: Proceedings of the IEEE International Conference on computer vision, pp 437–445, 2017

  22. Gionis A, Indyk P, Motwani R et al (1999) Similarity search in high dimensions via hashing. In Vldb 99:518–529

    Google Scholar 

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

    Article  Google Scholar 

  24. Dai B, Guo R, Kumar S, He N, Song L (2017) Stochastic generative hashing. In: Proceedings of the 34th International Conference on machine learning-volume 70, pp 913–922. JMLR.org, 2017

  25. Zhang D, Wang J, Cai D, Lu J (2010) Self-taught hashing for fast similarity search. In: Proceedings of the 33rd International ACM SIGIR Conference on research and development in information retrieval, pp 18–25. ACM, 2010

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

  27. Cao Y, Long M, Liu B, Wang J (2018) Deep cauchy hashing for hamming space retrieval. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 1229–1237, 2018

  28. Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300

    Article  Google Scholar 

  29. Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbor algorithm. IEEE Trans Syst Man Cybern 4:580–585

    Article  Google Scholar 

  30. Zhang Z, Lyons M, Schuster M, Akamatsu S (1998) Comparison between geometry-based and Gbor-wavelets-based facial expression recognition using multi-layer perceptron. In: Proceedings Third IEEE International Conference on automatic face and gesture recognition, pp 454–459. IEEE, 1998

  31. Zhang W, Kang P, Fang X, Teng L, Han N (2019) Joint sparse representation and locality preserving projection for feature extraction. Int J Mach Learn Cybern 10(7):1731–1745

    Article  Google Scholar 

  32. Nanni L, Ghidoni S, Brahnam S (2017) Handcrafted vs. non-handcrafted features for computer vision classification. Pattern Recognit 71:158–172

    Article  Google Scholar 

  33. Liao R, Shiqi Yu, An W, Huang Y (2020) A model-based gait recognition method with body pose and human prior knowledge. Pattern Recognit 98:107069

    Article  Google Scholar 

  34. LeCun Y, Bottou L, Bengio Y, Haffner P et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  35. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, vol 25. pp 1097–1105

  36. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 2818–2826, 2016

  37. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  38. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 770–778, 2016

  39. Liao R, An W, Li Z, Bhattacharyya SS (2021) A novel view synthesis approach based on view space covering for gait recognition. Neurocomputing 453:13–25

    Article  Google Scholar 

  40. Zhang Z, Chen Y, Saligrama V (2016) Efficient training of very deep neural networks for supervised hashing. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 1487–1495, 2016

  41. Zhang S, Liu S, Cao X, Song Z, Zhou J (2018) Watch fashion shows to tell clothing attributes. Neurocomputing 282:98–110

    Article  Google Scholar 

  42. Liu J, Song X, Chen Z, Ma J (2019) Neural fashion experts: I know how to make the complementary clothing matching. Neurocomputing 359:249–263

    Article  Google Scholar 

  43. Liu L, Zhang H, Ji Y, Jonathan Wu QM (2019) Toward ai fashion design: An attribute-gan model for clothing match. Neurocomputing 341:156–167

    Article  Google Scholar 

  44. Dmochowski JP, Sajda P, Parra LC (2010) Maximum likelihood in cost-sensitive learning: model specification, approximations, and upper bounds. J Mach Learn Res 11:3313–3332

    MathSciNet  MATH  Google Scholar 

  45. Nl Johnson S, Kotz N Balakrishnan (1970) Continuous univariate distributions, vol 1. Houghton Mifflin, Boston

    Google Scholar 

  46. Willliam F (2008) An introduction to probability theory and its applications, vol 2. Wiley, Hoboken

    Google Scholar 

  47. Morgado P, Li Y, Pereira JC, Saberian M, Vasconcelos N (2021) Deep hashing with hash-consistent large margin proxy embeddings. Int J Comput Vis 129(2):419–438

    Article  Google Scholar 

  48. Wang Y, Xianfeng O, Liang J, Sun Z (2020) Deep semantic reconstruction hashing for similarity retrieval. IEEE Trans Circ Syst Video Technol 31(1):387–400

    Article  Google Scholar 

  49. Li Y, Cao L, Zhu J, Luo J (2017) Mining fashion outfit composition using an end-to-end deep learning approach on set data. IEEE Trans Multimed 19(8):1946–1955

    Article  Google Scholar 

  50. Liu S, Feng J, Domokos C, Hui X, Huang J, Zhenzhen H, Yan S (2013) Fashion parsing with weak color-category labels. IEEE Trans Multimed 16(1):253–265

    Article  Google Scholar 

  51. Srinivasan K, Dastoor PH, Radhakrishnaiah P, Jayaraman S (1992) Fdas: a knowledge-based framework for analysis of defects in woven textile structures. J Text Inst 83(3):431–448

    Article  Google Scholar 

  52. 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  Google Scholar 

  53. Cao Y, Long M, Wang J, Zhu H, Wen Q (2016) Deep quantization network for efficient image retrieval. In: Thirtieth AAAI Conference on artificial intelligence, 2016

  54. Liu H, Wang R, Shan S, Chen X (2016) Deep supervised hashing for fast image retrieval. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 2064–2072, 2016

  55. Zhang Z, Zou Q, Lin Y, Chen L, Wang S (2019) Improved deep hashing with soft pairwise similarity for multi-label image retrieval. IEEE Trans Multimed 22(2):540–553

    Article  Google Scholar 

  56. Krizhevsky A, Hinton G et al (2009) Learning multiple layers of features from tiny images. Technical report, Citeseer

  57. Russakovsky O, Deng J, Hao S, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  58. Chua T-S, 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 the ACM International Conference on image and video retrieval, p 48. ACM, 2009

  59. Tianchi database obtained from ai challenge in xuelang manufacturing: Visual computing assisted quality inspection. https://tianchi.aliyun.com/competition/entrance/231666/%20introduction. Accessed 10 July 2018

  60. Fabric database collected from the competition of Guangdong industrial intelligence innovation. http://bbs.cvmart.net/topics/706/tianchi. Accessed 8 Aug 2019

  61. Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv:1708.07747

  62. Liu Z, Luo P, Qiu S, Wang X, Tang X (2016) Deepfashion: powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of IEEE Conference on computer vision and pattern recognition, pp 1096–1104

Download references

Acknowledgements

This research is supported by Laboratory for Artificial Intelligence in Design (Project Code: RP3-4) under InnoHK Research Clusters, Hong Kong SAR Government.

Author information

Author notes

  1. Xianjing Liu

    Present address: Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands

Authors and Affiliations

  1. Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China

    Dongmei Mo & Wai Keung Wong

  2. Laboratory for Artificial Intelligence in Design, New Territories, Hong Kong SAR, China

    Wai Keung Wong

  3. Department of Electronic Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China

    Yao Ge

Authors
  1. Dongmei Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wai Keung Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xianjing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yao Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wai Keung Wong.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 365 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mo, D., Wong, W.K., Liu, X. et al. Concentrated hashing with neighborhood embedding for image retrieval and classification. Int. J. Mach. Learn. & Cyber. 13, 1571–1587 (2022). https://doi.org/10.1007/s13042-021-01466-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-021-01466-7

Keywords

Search

Navigation