The clothing fashion reflects the common aesthetics that people share with each other in dressing. To recognize the fashion time of a clothing is meaningful for both an individual and the industry. In this paper, under the assumption that the clothing fashion changes year by year, the fashion-time recognition problem is mapped into a clothing-fashion classification problem. Specifically, a novel deep neural network is proposed which achieves accurate human body segmentation by fusing multi-scale convolutional features in a fully convolutional network, and then feature learning and fashion classification are performed on the segmented parts avoiding the influence of image background. In the experiments, 9,339 fashion images from 8 continuous years are collected for performance evaluation. The results demonstrate the effectiveness of the proposed body segmentation and fashion classification methods.
@article{arxiv.1803.03415,
title = {Fusing Hierarchical Convolutional Features for Human Body Segmentation and Clothing Fashion Classification},
author = {Zheng Zhang and Chengfang Song and Qin Zou},
journal= {arXiv preprint arXiv:1803.03415},
year = {2018}
}