Landmark-free Clothes Recognition with a Two-Branch Feature Selective Network

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In this Letter, the authors present a 'landmark-free' clothes recognition approach. Recent studies have shown that the use of landmark information has achieved great success in the task of clothes recognition. However, the landmark annotation is very labour intensive and time consuming. It also suffers from inter- and intra-individual variability. To overcome these problems, the authors propose a two-branch feature selective network for category classification and attribute prediction. Note that, in this Letter, they prove that the proposed network has an excellent ability to effectively learn a discriminative feature representation of a 'clothing image'. Experimental results on the benchmark data set show that the proposed network yields comparable performance to the state-of-the-art methods, which strongly depend on the fashion landmark.
Publisher
INST ENGINEERING TECHNOLOGY-IET
Issue Date
2019-06
Language
English
Article Type
Article
Citation

ELECTRONICS LETTERS, v.55, no.13, pp.745 - +

ISSN
0013-5194
DOI
10.1049/el.2019.0660
URI
http://hdl.handle.net/10203/263323
Appears in Collection
EE-Journal Papers(저널논문)
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