A Part-Based Deep Neural Network Cascade Model for Human Parsing

Yanghong Zhou, P. Y. Mok, Shijie Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Human parsing is important for image-based human-centric and clothing analyses. With the development of deep neural networks, some deep human parsing methods were recently proposed, which substantially improve the parsing accuracy. However, some localized small regions (such as sunglasses) are not parsed well in these methods. In this paper, we propose a Part-based Human Parsing Cascade (PHPC) to segment human images, imitating the observational mechanism of how people, when first looking at a human image, quickly scan the entire photograph to first locate the face and then the body parts to see what clothing the person is wearing. The observational mechanism of human vision is used to establish a cascade relationship in designing our network, in which a head-parsing sub-network and a body-parsing sub-network are integrated to the cascade of human parsing networks. The head-and body-parsing sub-networks focus on the head and body classes, respectively, and add attention to the head and body in the final neural networks. Comprehensive evaluations on the ATR dataset have demonstrated the effectiveness of our method.

Original languageEnglish
Article number8890627
Pages (from-to)160101-160111
Number of pages11
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 4 Nov 2019

Keywords

  • convolutional neural networks
  • deep learning
  • fashion parsing
  • Human parsing
  • image segmentation
  • image understanding

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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