Accurate Fashion Style Estimation with a Novel Training Set and Removal of Unnecessary Pixels
Ryusuke Miyamoto, Takeshi Nakajima, Takuro Oki · 2019
To improve the accuracy of fashion style estimation, this paper proposes a novel large-scale dataset named WEARStyle and two types of novel schemes that remove unnecessary pixels: SSD-based human detection and PSPNet-based pixel selection. The classification accuracy of the Hipster Wars dataset is improved to 78.8% by an SVM-based classifier when the WEARStyle dataset is used to train a ResNet50-based feature extractor. The accuracy is improved to 80.0% and 80.9%, when the SSD-based human detection and PSPNet-based pixel selection are applied, respectively. The achieved accuracy outperforms those of other existing schemes.