Towards color compatibility in fashion using machine learning

University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

Author: Xinhui Wang; [2019]

Keywords: ;

Abstract: Fashion analyses, such as predicting trends and fashion recommendation, have been a hot topic. Color, as one of the dominant features of clothing, has great influence on people’s shopping behaviors. Understanding popular colors and color combinations are of high business value. In this thesis, we investigate compatible color combinations in fashion. We tackle this problem in two parts. First, we implement a semantic segmentation model of fashion images to segment different clothing items of daily photos. We employ Deeplab V2 trained on ModaNet dataset, reaching 0.64 mIoU and 0.96 accuracy in the test set. Our experimental results achieve the state-of-the-art performance comparing to other models proposed in this field. Second, we propose two color recommendation approaches, matrix factorization and item-to-item collaborative filtering, in order to study color combinations in fashion and possibly make recommendations based on the study outcomes. The item-to-item collaborative filtering model shows the compatibility between/among colors quantitatively and achieves high-quality color recommendations with a hit-rate of 0.49.

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