Journal of the Korean Society of Clothing and Textiles (한국의류학회지)

The Korean Society of Clothing and Textiles (한국의류학회)
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Color & Texture Attribute Classification System of Fashion Item Image for Standardizing Learning Data in Fashion AI

패션 AI의 학습 데이터 표준화를 위한 패션 아이템 이미지의 색채와 소재 속성 분류 체계

  • Park, Nanghee (Dept. of Clothing & Textiles, Chungnam National University) ;
  • Choi, Yoonmi (Dept. of Clothing & Textiles, Chungnam National University)
  • Received : 2020.02.26
  • Accepted : 2020.03.31
  • Published : 2020.04.30
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Abstract

Accurate and versatile image data-sets are essential for fashion AI research and AI-based fashion businesses based on a systematic attribute classification system. This study constructs a color and texture attribute hierarchical classification system by collecting fashion item images and analyzing the metadata of fashion items described by consumers. Essential dimensions to explain color and texture attributes were extracted; in addition, attribute values for each dimension were constructed based on metadata and previous studies. This hierarchical classification system satisfies consistency, exclusiveness, inclusiveness, and flexibility. The image tagging to confirm the usefulness of the proposed classification system indicated that the contents of attributes of the same image differ depending on the annotator that require a clear standard for distinguishing differences between the properties. This classification system will improve the reliability of the training data for machine learning, by providing standardized criteria for tasks such as tagging and annotating of fashion items.

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References

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