Fashion Evaluation Method for Clothing Recommendation Based on Weak Appearance Feature

Author:

Zhang Yan12ORCID,Liu Xiang1ORCID,Shi Yunyu1ORCID,Guo Yunqi3,Xu Chaoqun3,Zhang Erwen4,Tang Jiaxun1,Fang Zhijun1ORCID

Affiliation:

1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

2. School of Fashion Technology, Shanghai University of Engineering Science, Shanghai 201620, China

3. School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, China

4. School of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Abstract

With the rapid rising of living standard, people gradually developed higher shopping enthusiasm and increasing demand for garment. Nowadays, an increasing number of people pursue fashion. However, facing too many types of garment, consumers need to try them on repeatedly, which is somewhat time- and energy-consuming. Besides, it is difficult for merchants to master the real-time demand of consumers. Herein, there is not enough cohesiveness between consumer information and merchants. Thus, a novel fashion evaluation method on the basis of the appearance weak feature is proposed in this paper. First of all, image database is established and three aspects of appearance weak feature are put forward to characterize the fashion level. Furthermore, the appearance weak features are extracted according to the characters’ facial feature localization method. Last but not least, consumers’ fashion level can be classified through support vector product, and the classification is verified with the hierarchical analysis method. The experimental results show that consumers’ fashion level can be accurately described based on the indexes of appearance weak feature and the approach has higher application value for the clothing recommendation system.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Computer Science Applications,Software

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