標題: Garment Detectives: Discovering Clothes and Its Genre in Consumer Photos
作者: Hidayati, Shintami C.
Hua, Kai-Lung
Tsao, Yu
Shuai, Hong-Han
Liu, Jiaying
Cheng, Wen-Huang
電子工程學系及電子研究所
電機工程學系
Department of Electronics Engineering and Institute of Electronics
Department of Electrical and Computer Engineering
公開日期: 1-Jan-2019
摘要: 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 multi-modality 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.
URI: http://dx.doi.org/10.1109/MIPR.2019.00095
http://hdl.handle.net/11536/153292
ISBN: 978-1-7281-1198-8
DOI: 10.1109/MIPR.2019.00095
期刊: 2019 2ND IEEE CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2019)
起始頁: 471
結束頁: 474
Appears in Collections:Conferences Paper