Who Leads the Clothing Fashion: Style, Color, or Texture? A Computational Study
This work addresses a specific problem in computational fashion analysis for researchers and industry professionals, but it is incremental as it applies existing methods to a new domain.
The study tackled the problem of determining which visual stimuli (style, color, texture) most influence clothing fashion updates by using a classification-based model to quantify their impact based on accuracy in fashion classification. The result showed that style has the highest influence, followed by color, and then texture.
It is well known that clothing fashion is a distinctive and often habitual trend in the style in which a person dresses. Clothing fashions are usually expressed with visual stimuli such as style, color, and texture. However, it is not clear which visual stimulus places higher/lower influence on the updating of clothing fashion. In this study, computer vision and machine learning techniques are employed to analyze the influence of different visual stimuli on clothing-fashion updates. Specifically, a classification-based model is proposed to quantify the influence of different visual stimuli, in which each visual stimulus's influence is quantified by its corresponding accuracy in fashion classification. Experimental results demonstrate that, on clothing-fashion updates, the style holds a higher influence than the color, and the color holds a higher influence than the texture.