Skip to main content

Advertisement

Springer Nature Link
Log in

How visual chirality affects the performance of image hashing

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Visual chirality reveals the phenomenon that chiral data will present different semantics after flipping. Although image flipping is widely used in image hashing learning as a data augmentation technique, the effect of learning chiral image data on hashing performance has not been fully discussed. To explore this issue, this paper first designs an approach to recognize images with chiral cues, then constructs the chiral datasets including different proportions of images with chiral cues, and finally analyzes and discusses the performance change via testing three representative image hashing methods with different hash code lengths on constructed chiral datasets. In addition, to understand the effect of visual chirality from an internal perspective, we illustrate visual results of activated regions between some original images with chiral cues and their flipped ones. We conduct the above experiments on three public image datasets including VOC2007, MS-COCO, and NUS-WIDE. Experimental results reveal that different proportions of chiral data will greatly affect the performance of image hashing and the best performance appears when the proportion of images with chiral cues accounts for 15%\(\sim\)25% or 75%\(\sim\)85%. The code of this work is released at: https://github.com/lzHZWZ/Visual_Chirality_Hashing.

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data availability

The datasets generated during and/or analyzed during the current study are available in the open-source github repository: https://github.com/lzHZWZ/Visual_Chirality_Hashing.

References

  1. Wang Y, Song J, Zhou K, Liu Y (2021) Unsupervised deep hashing with node representation for image retrieval. Pattern Recognit 112:107785. https://doi.org/10.1016/j.patcog.2020.107785

    Article  Google Scholar 

  2. Zhou K, Liu Y, Song J, Yan L, Zou F, Shen F (2015) Deep self-taught hashing for image retrieval. In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, MM, pp 1215–1218. ACM, Brisbane. https://doi.org/10.1145/2733373.2806320

  3. Liu Y, Wang Y, Song J, Guo C, Zhou K, Xiao Z (2020) Deep self-taught graph embedding hashing with pseudo labels for image retrieval. In: IEEE International Conference on Multimedia and Expo, ICME, pp 1–6. IEEE, London. https://doi.org/10.1109/ICME46284.2020.9102819

  4. Liu Y, Song J, Zhou K, Yan L, Liu L, Zou F, Shao L (2019) Deep self-taught hashing for image retrieval. IEEE Trans Cybern 49(6):2229–2241. https://doi.org/10.1109/ICME46284.2020.9102819

    Article  Google Scholar 

  5. Hoorick BV, Vondrick C (2021) Dissecting image crops. In: 2021 IEEE/CVF International Conference on Computer Vision, ICCV, pp 9721–9730. IEEE, Montreal. https://doi.org/10.1109/ICCV48922.2021.00960

  6. Jurio A, Pagola M, Galar M, Lopez-Molina C, Paternain D (2010) A comparison study of different color spaces in clustering based image segmentation. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 13th International Conference, IPMU. Communications in Computer and Information Science, vol. 81, pp 532–541. Springer, Dortmund. https://doi.org/10.1007/978-3-642-14058-7_55

  7. Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI, pp 13001–13008. AAAI Press, New York. https://ojs.aaai.org/index.php/AAAI/article/view/7000

  8. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y (2014) Generative adversarial networks. CoRR arXiv:abs/1406.2661

  9. Frid-Adar M, Diamant I, Klang E, Amitai M, Goldberger J, Greenspan H (2018) Gan-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321:321–331. https://doi.org/10.1016/j.neucom.2018.09.013

    Article  Google Scholar 

  10. Lim SK, Loo Y, Tran N, Cheung N, Roig G, Elovici Y (2018) DOPING: generative data augmentation for unsupervised anomaly detection with GAN. In: IEEE International Conference on Data Mining, ICDM, pp. 1122–1127. IEEE Computer Society, Singapore. https://doi.org/10.1109/ICDM.2018.00146

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

    MATH  Google Scholar 

  12. Lin Z, Sun J, Davis A, Snavely N (2020) Visual chirality. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp 12292–12300. Computer Vision Foundation / IEEE, Seattle. https://doi.org/10.1109/CVPR42600.2020.01231

  13. Zheng Y, Zhang Y, Xu X, Wang J, Yao H (2021) Visual chirality meets freehand sketches. In: 2021 IEEE International Conference on Image Processing, ICIP, pp 1544–1548. IEEE, Anchorage. https://doi.org/10.1109/ICIP42928.2021.9506772

  14. He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. CoRR arXiv:abs/1512.03385

  15. Everingham M, Gool LV, Williams CKI, Winn JM, Zisserman A (2010) The Pascal visual object classes (VOC) challenge. Int J Comput Vis 88(2):303–338. https://doi.org/10.1007/s11263-009-0275-4

    Article  Google Scholar 

  16. Tsung-Yi Lin SJB Michael Maire et al. (2014) Microsoft COCO: common objects in context. In: Computer Vision - ECCV 2014 - 13th European Conference. Lecture Notes in Computer Science, vol. 8693, pp 740–755. Springer, Zurich. https://doi.org/10.1007/978-3-319-10602-1_48

  17. Chua T, 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 8th ACM International Conference on Image and Video Retrieval, CIVR. ACM, Santorini Island. https://doi.org/10.1145/1646396.1646452

  18. Cao Z, Long M, Wang J, Yu PS (2017) Hashnet: deep learning to hash by continuation. In: IEEE International Conference on Computer Vision, ICCV, pp 5609–5618. IEEE Computer Society, Venice. https://doi.org/10.1109/ICCV.2017.598

  19. Cao Y, Long M, Liu B, Wang J (2018) Deep cauchy hashing for hamming space retrieval. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp 1229–1237. Computer Vision Foundation / IEEE Computer Society, Salt Lake City. https://doi.org/10.1109/CVPR.2018.00134

  20. Jean-Bastien Grill FA Florian Strub et al. (2020) Bootstrap your own latent –a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, Virtual. https://proceedings.neurips.cc/paper/2020/hash/f3ada80d5c4ee70142b17b8192b2958e-Abstract.html

  21. Pickup LC, Pan Z, Wei D, Shih Y, Zhang C, Zisserman A, Schölkopf B, Freeman WT (2014) Seeing the arrow of time. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp 2043–2050. IEEE Computer Society, Columbus. https://doi.org/10.1109/CVPR.2014.262

  22. Wei D, Lim JJ, Zisserman A, Freeman WT (2018) Learning and using the arrow of time. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp 8052–8060. Computer Vision Foundation / IEEE Computer Society, Salt Lake City. https://doi.org/10.1109/CVPR.2018.00840

  23. Liu Z, Zhang J, Liu L (2016) Upright orientation of 3d shapes with convolutional networks. Graph Model 85:22–29. https://doi.org/10.1016/j.gmod.2016.03.001

    Article  MathSciNet  Google Scholar 

  24. Krippendorf S, Syvaeri M (2021) Detecting symmetries with neural networks. Mach Learn Sci Technol 2(1):15010. https://doi.org/10.1088/2632-2153/abbd2d

    Article  Google Scholar 

  25. Barenboim G, Hirn J, Sanz V (2021) Symmetry meets AI. CoRR arXiv:abs/2103.06115

  26. Zhang Z, Zhang F, Chen H, Liu J, Wang H, Dai G (2014) Left and right hand distinction for multi-touch tabletop interactions. In: 19th International Conference on Intelligent User Interfaces, IUI, pp 47–56. ACM, Haifa. https://doi.org/10.1145/2557500.2557525

  27. Indyk P, Motwani R (1998) Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on the Theory of Computing, pp 604–613. ACM, Dallas. https://doi.org/10.1145/276698.276876

  28. Shen Y, Qin J, Chen J, Yu M, Liu L, Zhu F, Shen F, Shao L (2020) Auto-encoding twin-bottleneck hashing. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, pp 2815–2824. Computer Vision Foundation / IEEE, Seattle. https://doi.org/10.1109/CVPR42600.2020.00289

  29. Ng T, Balntas V, Tian Y, Mikolajczyk K (2020) SOLAR: second-order loss and attention for image retrieval. In: Computer Vision - ECCV 2020 - 16th European Conference. Lecture Notes in Computer Science, vol. 12370, pp 253–270. Springer, Glasgow. https://doi.org/10.1007/978-3-030-58595-2_16

  30. Feng J, Karaman S, Chang S (2017) Deep image set hashing. In: 2017 IEEE Winter Conference on Applications of Computer Vision, WACV, pp 1241–1250. IEEE Computer Society, Santa Rosa. https://doi.org/10.1109/WACV.2017.143

  31. Lin K, Lu J, Chen C, Zhou J (2016) Learning compact binary descriptors with unsupervised deep neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp 1183–1192. IEEE Computer Society, Las Vegas. https://doi.org/10.1109/CVPR.2016.133

  32. Li Y, Wang Y, Miao Z, Wang J, Zhang R (2020) Contrastive self-supervised hashing with dual pseudo agreement. IEEE Access 8:165034–165043. https://doi.org/10.1109/ACCESS.2020.3022672

    Article  Google Scholar 

  33. Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp 248–255. IEEE Computer Society, Miami. https://doi.org/10.1109/CVPR.2009.5206848

  34. You Y, Gitman I, Ginsburg B (2017) Scaling SGD batch size to 32k for imagenet training. CoRR arXiv:abs/1708.03888

  35. Adam Paszke FM Sam Gross, et al. (2019) Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, Vancouver, pp 8024–8035. https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html

Download references

Acknowledgements

Thanks for the support of the National Natural Science Foundation of China No. 61902135 and the National Natural Science Foundation of China Grant No. 62232007. This work was also achieved in Key Laboratory of Information Storage System and Ministry of Education of China.

Author information

Author notes

  1. Guangxing Hu contributed equally to this work.

Authors and Affiliations

  1. Wuhan National Laboratory for Opto-electronics, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, 430074, Hubei, China

    Yanzhao Xie, Guangxing Hu, Yu Liu & Ke Zhou

  2. School of Computer Science and Technology, Huazhong University of Science and Technology, Luoyu Road 1037, Wuhan, 430074, Hubei, China

    Yu Liu

  3. Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania, 15213-3890, USA

    Zhiqiu Lin

  4. Institute of Information Engineering, Chinese Academy of Sciences, Minzhuang Road 89, Beijing, 100093, China

    Yuhong Zhao

Authors
  1. Yanzhao Xie
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Guangxing Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yu Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Zhiqiu Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Ke Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Yuhong Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to Yu Liu or Yuhong Zhao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, Y., Hu, G., Liu, Y. et al. How visual chirality affects the performance of image hashing. Neural Comput & Applic 35, 9003–9016 (2023). https://doi.org/10.1007/s00521-022-08141-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08141-0

Keywords