Context-aware encoding for clothing parsing

C. H. Yoo, Y. G. Shin, S. W. Kim, S. J. Ko

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Clothing parsing is a special type of semantic segmentation in which each pixel is assigned with clothing labels. Unlike general scene semantic segmentation, stylish match (e.g. skirts + blouse, jeans + T-shirt) is an important cue for recognising fine-grained categories in clothing parsing. In this Letter, the authors propose a context-aware outfit encoder (COE), as a side branch, that drives the convolutional neural network to take the stylish match into account for clothing parsing. The proposed COE provides information on matching clothes that can be utilised to improve the prediction accuracy of the base network significantly. Experimental results show that fully convolutional network and MobileNet with the COE improve the mean intersection of the union of those without the COE by 2.5 and 2.8%, respectively, on CFPD dataset.

Original languageEnglish
Pages (from-to)692-693
Number of pages2
JournalElectronics Letters
Issue number12
Publication statusPublished - 2019 Jun 13

Bibliographical note

Funding Information:
Acknowledgments: This research was funded and supported by LG Display CO., Ltd.

Publisher Copyright:
© 2019 The Institution of Engineering and Technology.

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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