Title:	DESCRIBING CLOTHING IN HUMAN IMAGES: A PARSING-POSE INTEGRATED APPROACH
Author(s):	Yanghong Zhou, Runze Li, Yangping Zhou and Pik-Yin Mok
ISBN:	978-989-8533-79-1
Editors:	Katherine Blashki and Yingcai Xiao
Year:	2018
Edition:	Single
Keywords:	Clothing Retrieval, Clothing Recognition, Fine-Grained Classification, Pose Estimation, Human Parsing, Deep Learning
Type:	Full Paper
First Page:	205
Last Page:	213
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	With the advent of information technology, digital product information grows exponentially. People are exposed to far too much information, and information overload can slow down, instead of speeding up, a simple decision-making process like searching for suitable clothing online. Traditional semantic-based product retrieval may not be effective due to human subjectivity and cognitive differences. In this paper, we propose a method by integrating the state-of-the-arts deep neural models in pose estimation, human parsing and category classification to recognise from human images all clothing items and their fine-grained product category information. The proposed fine-grained clothing classification model can facilitate a wide range of applications such as the automatic annotation of clothing images. The effectiveness of the proposed method is validated through experiment on a real-world dataset.
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