关键词: 服装图像, 深度学习, 图像分类, 基于区域的全卷积网络（R-FCN）, HyperNet, 区域建议网络, 空间转换网络

Abstract: Nowadays, the number of clothing images on the Internet is growing rapidly, and the demand for intelligent classification of a large number of clothing images is increasing. R-FCN（Region-Based Fully Convolution Networks） is introduced into the garment image recognition. In order to solve the problems of long training time and low recognition rate of the deformed garment in the garment image classification, a novel improved framework HSR-FCN is proposed. Based on R-FCN, the new framework integrates the HyperNet network with the region proposal network, changes the learning mode of image features, so that the HSR-FCN can achieve a higher accuracy in shorter training time. Besides, the spatial transformation network is introduced into the model, and the transformation and alignment of input image and feature map are carried out, which strengthens the learning of multi-angle clothing. The experimental results show that the improved HSR-FCN network model effectively strengthens the learning situation of the deformed garment image, and in the case of shorter training time, the average accuracy of the original network model R-FCN is about three percentage points higher than that of the original network model, up to 96.69%.

Key words: garment images, deep learning, image classification, Region-Based Fully Convolutional Networks（R-FCN）, HyperNet, region proposal networks, spatial transformation networks