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http://dx.doi.org/10.5850/JKSCT.2020.44.2.354

Color & Texture Attribute Classification System of Fashion Item Image for Standardizing Learning Data in Fashion AI  

Park, Nanghee (Dept. of Clothing & Textiles, Chungnam National University)
Choi, Yoonmi (Dept. of Clothing & Textiles, Chungnam National University)
Publication Information
Journal of the Korean Society of Clothing and Textiles / v.44, no.2, 2020 , pp. 354-368 More about this Journal
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.
Keywords
Classification system; Color; Fashion AI; Image data-sets; Texture;
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Times Cited By KSCI : 13  (Citation Analysis)
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