Advertisement

Fashion Style Recognition with Graph-Based Deep Convolutional Neural Networks

  • Cheng Zhang
  • Xiaodong Yue
  • Wei Liu
  • Can Gao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)

Abstract

Recognizing fashion styles of clothing from images plays an important role in the application scenarios of clothing retrieval and recommendation in E-commerce. Most existing works directly utilize the machine learning methods such as Deep Convolutional Neural Network (DCNN) to classify clothing images into different styles. However, these image classification methods are totally data-driven and neglect the domain issues of apparel fashion design. To tackle this problem, we propose a domain-driven clothing style recognition method in this paper, which involves both image classification and domain knowledge of fashion design. Specifically, we formulate the domain knowledge of design elements with the undirected graphs of clothing attributes and thereby build up a domain-driven fashion style classifier with Graph-Based DCNN. Synthesizing the classifications based on both clothing images and the graphs of design elements, we produce the final clothing style recognition results. The experiments on Deep Fashion database validate that the proposed clothing style recognition method can achieve more precise results than the traditional data-driven image classification methods.

Keywords

Fashion style recognition Deep convolutional neural networks 

Notes

Acknowledgements

This work reported here was financially supported by the National Natural Science Foundation of China (Grant No. 61573235).

References

  1. 1.
    Liu, Z., Luo P., Qiu, S., Wang, X., Tang, X.: Deepfashion: powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1096–1104 (2016)Google Scholar
  2. 2.
    Huang, J., Feris, R., Chen, Q., Yan, S.: Cross-domain image retrieval with a dual attribute-aware ranking network. In: IEEE International Conference on Computer Vision, pp. 1062–1070 (2015)Google Scholar
  3. 3.
    Xiaodan, L., Liang, L., Wei, Y., Ping, L., Junshi, H., Yan, S.: Clothes co-parsing via joint image segmentation and labeling with application to clothing retrieval. IEEE Trans. Multimedia 18(6), 1175–1186 (2016)CrossRefGoogle Scholar
  4. 4.
    Qian, Y., Giaccone, P., Sasdelli, M., Vasquez, E., Sengupta, B.: Algorithmic clothing: hybrid recommendation, from street-style-to-shop (2017)Google Scholar
  5. 5.
    Hadi Kiapour, M., Han, X., Lazebnik, S., Berg, A.C., Berg, T.L.: Where to buy it: matching street clothing photos in online shops. In: IEEE International Conference on Computer Vision, pp. 3343–3351 (2015)Google Scholar
  6. 6.
    Liu, Z., Yan, S., Luo, P., Wang, X., Tang, X.: Fashion landmark detection in the wild. In: European Conference on Computer Vision, pp. 229–245 (2016)CrossRefGoogle Scholar
  7. 7.
    Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: International conference on machine learning, pp. 2014–2023 (2016)Google Scholar
  8. 8.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Computer Science (2014)Google Scholar
  9. 9.
    Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. AAAI (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Shanghai Institute for Advanced Communication and Data ScienceShanghai UniversityShanghaiChina
  2. 2.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina
  3. 3.Institute of Textiles and ClothingThe Hong Kong Polytechnic UniversityHong KongChina

Personalised recommendations