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Title: A part-based deep neural network cascade model for human parsing
Authors: Zhou, YH 
Mok, PY 
Zhou, SJ
Issue Date: 2019
Source: IEEE access, 4 Nov. 2019, v. 7, p. 160101-160111
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.
Keywords: Image segmentation
Feature extraction
Neural networks
Human parsing
Deep learning
Fashion parsing
Image segmentation
Image understanding
Convolutional neural networks
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2951182
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
The following publication Y. Zhou, P. Y. Mok and S. Zhou, "A Part-Based Deep Neural Network Cascade Model for Human Parsing," in IEEE Access, vol. 7, pp. 160101-160111, 2019 is available at
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