Affiliations: School of Humanities and Arts, Hunan International Economics University, Changsha, China
Correspondence:
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Corresponding author. Jing Wang, School of Humanities and Arts, Hunan International Economics University, Changsha, China. E-mail: 45419800@qq.com.
Abstract: The clothing images on the Internet is growing rapidly, and there is an increasing demand for the clothing images’ intelligent classification. In this paper, Region-Based Fully Convolutional Networks (R-FCN) is introduced into the clothing image recognition. In the clothing image classification, because the network training time is long and the recognition rate of deformed clothing images is low, an improved framework HSR-FCN is proposed. The regional suggestion network and HyperNet network in R-FCN are integrated in the new framework, the learning approach of image features is changed in HSR-FCN, the higher accuracy can be achieved in a shorter training time. A spatial transformation network is introduced into the model, the input clothing image and feature map are spatially transformed and aligned, the feature learning is strengthened for multi-angle clothing and deformed clothing. The experimental results show that the improved HSR-FCN model is used to strengthen effectively the learning of deformed clothing images, and with a shorter training time, the average accuracy rate of the original network model R-FCN is increased by about 3%, it reachs 96.69%.
Keywords: Garment images, deep learning, image classification, region-based fully convolutional networks (R-FCN), HyperNet, region proposal networks, spatial transformation networks
