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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