@inproceedings{3ead754b8e664f759870f10cd3e780e6,
title = "Garment Detectives: Discovering Clothes and Its Genre in Consumer Photos",
abstract = "Clothing image analysis has shown its potential for use in a wide range of applications such as personalized clothing recommendation. Given a consumer photo, this paper addresses the problem of finding clothes and recognizing the genre of that clothes. This problem is very challenging due to large variations of uncontrolled realistic imaging conditions. To tackle these challenges, we formulate a novel framework by integrating local features of multimodality as the instances of the price-collecting Steiner tree (PCST) problem to discover clothing regions, and exploiting visual style elements to discover the clothing genre. The experimental results show that our fully automatic approach is effective to identify irregular shape of clothing region, and it significantly improves the accuracy of clothing genre recognition for images taken in unconstrained environment.",
keywords = "Clothing image analysis, clothing genres, detection and recognition, visual style elements",
author = "Hidayati, {Shintami Chusnul} and Hua, {Kai Lung} and Yu Tsao and Shuai, {Hong Han} and Jiaying Liu and Cheng, {Wen Huang}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019 ; Conference date: 28-03-2019 Through 30-03-2019",
year = "2019",
month = apr,
day = "22",
doi = "10.1109/MIPR.2019.00095",
language = "English",
series = "Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "471--474",
booktitle = "Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019",
address = "United States",
}