Comparing Clothing Styles by Means of Computer Vision Methods

  • Paweł Forczmański
  • Piotr Czapiewski
  • Dariusz Frejlichowski
  • Krzysztof Okarma
  • Radosław Hofman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8671)


The paper deals with a problem of comparing and retrieving visual data representing various clothing styles. Proposed solution joins several sophisticated computer vision methods, such as face detection using AdaBoost strategy, human body segmentation and decomposition using pictorial structures and appearance models, together with visual descriptors employing simplified dominant color descriptor. The input images do not necessary have to be taken in controlled environment, so the flexibility of the system is high. The proposed algorithm makes it possible to compare images presenting humans and retrieve images with similar clothing style. The potential application is the area of social network services, mostly recommendation web-based systems, that help people choose clothes and share with clothing ideas. Developed algorithm has been tested on 650 images gathered from various social media in the Internet and showed high accuracy rate.


  1. 1.
    Zhang, W., Begole, B., Chu, M., Liu, J., Yee, N.: Real-time clothes comparison based on multi-view vision. In: Second ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2008, pp. 1–10. IEEE (2008)Google Scholar
  2. 2.
    Di, W., Wah, C., Bhardwaj, A., Piramuthu, R., Sundaresan, N.: Style Finder: Fine-Grained Clothing Style Detection and Retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013, pp. 8–13. IEEE (2013)Google Scholar
  3. 3.
    Chen, H., Gallagher, A., Girod, B.: Describing clothing by semantic attributes. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 609–623. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Anguelov, D., Lee, K., Gokturk, S.B., Sumengen, B.: Contextual identity recognition in personal photo albums. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–7. IEEE (2007)Google Scholar
  5. 5.
    Song, Y., Leung, T.: Context-aided human recognition – clustering. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 382–395. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Liu, S., Song, Z., Liu, G., Xu, C., Lu, H., Yan, S.: Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012, pp. 3330–3337. IEEE (2012)Google Scholar
  7. 7.
  8. 8.
    Ramanan, D., Forsyth, D.A., Zisserman, A.: Tracking People by Learning their Appearance. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 65–81 (2007)CrossRefGoogle Scholar
  9. 9.
    Ramanan, D.: Part-based Models for Finding People and Estimating Their Pose. In: Moeslund, T., Hilton, A., Krüger, V., Sigal, L. (eds.) Visual Analysis of Humans, pp. 199–223. Springer, London (2011)Google Scholar
  10. 10.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2001, pp. I-511–I-518. IEEE (2001)Google Scholar
  11. 11.
    Roerdink, J., Meijster, A.: The Watershed Transform: Defnitions. Algorithms and Parallelization Strategies Fundamenta Informaticae 41, 187–228 (2001)MathSciNetGoogle Scholar
  12. 12.
    Eichner, M., Ferrari, V.: Better Appearance Models for Pictorial Structures. In: Proceedings of British Machine Vision Conference (2009)Google Scholar
  13. 13.
    Eichner, M., Marin-Jimenez, M., Zisserman, A., Ferrari, V.: Articulated Human Pose Estimation and Search in (Almost) Unconstrained Still Images. Technical Report No. 272. ETH Zurich, D-ITET, BIWI (2010)Google Scholar
  14. 14.
  15. 15.
    Yamada, A., Pickering, M., Jeannin, S., Cieplinski, L., Jens, R.O., Kim, M.: MPEG-7 Visual Part of Experimentation Model Version 9.0 - Part 3 Dominant Color. ISO/IEC JTC1/SC29/WG11/N3914 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Paweł Forczmański
    • 1
  • Piotr Czapiewski
    • 1
  • Dariusz Frejlichowski
    • 1
  • Krzysztof Okarma
    • 2
  • Radosław Hofman
    • 3
  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of Technology, SzczecinSzczecinPoland
  2. 2.Faculty of Electrical EngineeringWest Pomeranian University of Technology, SzczecinSzczecinPoland
  3. 3.FireFrog Media sp. z o.o.PoznańPoland

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