Conditional progressive network for clothing parsing. Issue 4 (11th March 2019)

• Record Type: Journal Article
• Authors: Su, Zhuo; Guo, Jiaming; Zhang, Gengwei; Luo, Xianghui; Wang, Ruomei; Zhou, Fan
• Is Part Of: IET image processing. Volume 13:Issue 4(2019)
• Page Start: 556
• Page End: 565
• Publication Date: 2019-03-11
• Subjects: learning (artificial intelligence) -- clothing -- pose estimation -- production engineering computing; complicated mutual interference -- Conditional Progressive Network -- Conditional Parsing Network -- Pose Estimation Network -- Label Transform Network -- intermediate parsing result -- multiple progressive stages -- fashion clothing cases -- Conditional progressive network -- clothing parsing methods; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5494
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Clothing parsing is significant to many clothing applications. Recently, a lot of clothing parsing methods have been presented, which explore the innovation of the parsing pipeline or try to find more specific prior information. Although these methods perform well in some benchmarks, a few challenging problems have not been solved yet, such as the complicated mutual interference among labels. In this study, the authors propose a Conditional Progressive Network to parse clothing in different scales and prevent the mutual interference among labels. The authors' solution consists of three sub‐networks, including Conditional Parsing Network (CPN), Pose Estimation Network (PEN) and Label Transform Network (LTN). Specifically, the CPN module generates the intermediate parsing result in the form of the multiple progressive stages, which combines with the previous outputs in each stage and the specific prior conditions. The PEN module provides a series of heat maps about the human pose information. The LTN module suppresses the redundant labels to avoid the mutual interference among labels. They demonstrate their solution in parsing the fashion clothing cases on the ATR and the Fashion dataset. In their experiments, their method obtains a better performance than the state‐of‐the‐art methods.