Skip to main content
SpringerLink
Log in

Understanding minority costumes: a computer vision perspective

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

It is an extremely interesting work to understand the minority costumes in computer vision and ethnology community. It explored some crucial clue for understanding minority costumes via computer vision technology. As it known to all, complicated and subtle structure between different minority costumes lead it becomes hard work to recognize them with computer vision even people. An intelligent framework is proposed for understanding minority costumes from computer vision perspective in this paper. First, the images are converted into grayscale ones as the digital image processing pipeline; then, the grayscale images are segmented with the help of structured forests algorithm; after that, a new Revised Histogram of Oriented Gradient is proposed to compute the feature for each segmented gray minority costume image. At the last, the random forests method is used as the classifier for this minority costumes understanding intelligent system. For lack of acknowledged minority costume image data sets, we evaluated the performances of the proposed method on self-construct data set, and the experimental results are presented.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

(* The image from Internet)

Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Wu, D.Y.: The construction of Chinese and non-Chinese identities. Daedalus 120, 159–179 (1991)

    Google Scholar 

  2. Wu, L., He, Y., Jiang, B., et al.: The association between the prevalence, treatment and control of hypertension and the risk of mild cognitive impairment in an elderly urban population in China. Hypertens. Res. 39(5), 367–375 (2016)

    Article  Google Scholar 

  3. Xie, Y., Lu, P.: The sampling design of the China family panel studies (CFPS). Chin. J. Sociol. 1(4), 471–484 (2015)

    Article  Google Scholar 

  4. Corrigan, G.: Miao Textiles from China. British Museum Press, London (2001)

    Google Scholar 

  5. Xiao-yun, L.U.: The decorative arts symbol of Miao costumes. J. Nantong Univ. (Soc. Sci. Ed.) 25(5), 90–95 (2009)

    MathSciNet  Google Scholar 

  6. Pourret, J.G.: The Yao: The Mien and Mun Yao in China, North Vietnam. Laos and Thailand, Art Media Resources Limited, Chicago (2002)

    Google Scholar 

  7. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2012)

    MATH  Google Scholar 

  8. Nixon, M.S., Aguado, A.S.: Feature Extraction and Image Processing for Computer Vision. Academic Press, Cambridge (2012)

    Google Scholar 

  9. Zhang, Q., Xu, Y.: Block-based selection random forest for texture classification using multi-fractal spectrum feature. Neural Comput. Appl. 27(3), 593–602 (2016)

    Article  Google Scholar 

  10. Xing, L., Zhang, J., Liang, H., et al.: Intelligent recognition of dominant colors for Chinese traditional costumes based on a mean shift clustering method. J. Text. Inst. 109(10), 1304–1314 (2018)

    Article  Google Scholar 

  11. Dillon, M.: Majorities and minorities in China: an introduction. Ethn. Racial Stud. 39(12), 2079–2090 (2016)

    Article  Google Scholar 

  12. Wang, F., Peng, H., Shi, W.: The relationship between air layers and evaporative resistance of male Chinese ethnic clothing. Appl. Ergon. 56, 194–202 (2016)

    Article  Google Scholar 

  13. Xu, Y., Zhang, Q., Wang, L.: Metric forests based on Gaussian mixture model for visual image classification. Soft. Comput. 22(2), 499–509 (2018)

    Article  Google Scholar 

  14. Shen, X.M., Zhou, J.X., Xu, T.W.: Minority costume image retrieval by fusion of color histogram and edge orientation histogram. In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), pp. 1–7. IEEE (2016)

  15. Liu, G.H., Li, Z.Y., Zhang, L., et al.: Image retrieval based on micro-structure descriptor. Pattern Recognit. 44(9), 2123–2133 (2011)

    Article  Google Scholar 

  16. Liu, G.H., Yang, J.Y.: Content-based image retrieval using color difference histogram. Pattern Recognit. 46(1), 188–198 (2013)

    Article  Google Scholar 

  17. Friedman, J., Hastie, T., Tibshirani, R.: The elements of statistical learning, 2nd edn. Springer, New York (2009)

    MATH  Google Scholar 

  18. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893. IEEE (2005)

  19. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  20. Dollár, P., Zitnick, C.L.: Fast edge detection using structured forests. IEEE Trans. Pattern Anal. Mach. Intell. 37(8), 1558–1570 (2015)

    Article  Google Scholar 

  21. Bourel, M., Fraiman, R., Ghattas, B.: Random average shifted histograms. Comput. Stat. Data Anal. 79, 149–164 (2014)

    Article  MathSciNet  Google Scholar 

  22. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 1097–1105 (2012)

Download references

Acknowledgements

The work is supported by National Natural Science Foundation of China (61802082, 61762020), Guizhou Science and Technology Project (QIAN KE HE J ZI [2014]2094), and Guizhou Province Department of education Project (QIAN JIAO HE KY[2017]129, QIAN JIAO HE KY[2018]018). The authors would like to thank ShengJu Jin for some previous work.

Author information

Authors and Affiliations

  1. School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, 550025, Guizhou, China

    Qian Zhang, Yu-cheng Yang, Shi-qin Yue, Ding-qin Shao & Lin Wang

  2. Academic Affairs Office, Guizhou Minzu University, Guiyang, 550025, Guizhou, China

    Qian Zhang

  3. Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou, Guiyang, 550025, Guizhou, China

    Lin Wang

Authors
  1. Qian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu-cheng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shi-qin Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ding-qin Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qian Zhang.

Additional information

Communicated by P. Pala.

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Q., Yang, Yc., Yue, Sq. et al. Understanding minority costumes: a computer vision perspective. Multimedia Systems 26, 191–200 (2020). https://doi.org/10.1007/s00530-019-00637-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-019-00637-5

Keywords

Search

Navigation