Deep Fashion Recommendation System with Style Feature Decomposition Recently, convolutional neural network (CNN) based fashion recommendation techniques, which automatically recommend the matching clothes to the consumer, have been widely researched. In general, the feature vector of a fashion item, i.e. clothes vector, obtained by CNN conveys two types of information: style and category, where the style indicates the distinctive characteristic of the clothes and the category represents the common properties of the clothes in the same class. Due to the mixed information of style and category, however, the clothes vector often recommends the unmatching clothes. To solve this problem, we propose a style feature extraction (SFE) layer, which effectively decomposes the clothes vector into style and category. Based on the characteristics that the category information has small variations in the same class while being distinguished from other classes, we extract and remove the category information from the clothes vector to obtain more accurate style information. Experimental results show that the proposed method achieves stateoftheart results in terms of link prediction, which is a performance measure of a stylish match. In addition, as a simple CNN layer, it is expected that the proposed SFE layer is compatible with all popular CNN architectures.

Accuracy Improvement of Fashion Style Estimation with Attention Control of a Classifier Computer vision has begun to be applied to fashion applications owing to the remarkable development of image recognition technology and deep learning. Fashion style estimation, the focus of this paper, is a hot topic as it provides useful information and services to consumers. This paper aims to enhance the accuracy of fashion style estimation by improving the classifier. To control the attention of a classifier, two different approaches are followed: human detection after segmentation with the aim of improving on previous schemes and enhancing the visual attention model (VAM). Experiments were conducted using the Hipster Wars dataset to train a final SVMbased classifier and the WEARStyle dataset to construct a feature extractor. The results using SSDbased detection after PSPNetbased segmentation, VAM with ICNet, and VAM with PSPNet showed classification accuracies of 83.8%, 84.7%, and 85.1%, respectively. All these outperformed VAM with full convolutional network (FCN), which resulted in a classification accuracy of 83.0% under the same experimental conditions. The results indicate that classification accuracy can be improved upon when the attention of the classifier is controlled.