  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.
