Fashion Style Recognition with Graph-Based Deep Convolutional Neural Networks

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


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.


Fashion style recognition Deep convolutional neural networks 



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


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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

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