Modeling Relationship between Visual Impression of Commodities and Their Graphical Features

Consumers feel heavy burden to find Commodities suited individual preferences owing to large quantity of them on the Web. Therefore, it is necessary to classify Commodities based on subjective preferences. This paper describes the method to model the relationship between subjective visual impressions and objective graphical features through machine learning for each user. The way to describe the visual impression is to use adjectives used by the professional photographers. As graphical feature vectors, we compute Lab color histogram and SURF from Commodity photos. We estimate the subjective visual impression of Commodities and classify them by constructing classifiers for the impression groups using Random Forests. As a result of the experiment, the proposed method achieved 80.1% accuracy on average.