@inproceedings{f571593a60ae49e38454a6f8b6aa694b,
title = "Attributes-oriented clothing description and retrieval with multi-task convolutional neural network",
abstract = "This paper seek answer to question how to search clothing when consumer pays attention to a part of clothing. A novel framework is proposed to solve above problem by attributes. First of all, Fast-RCNN detects person from complex background. Then a Convolutional Neural Network (CNN) is combined with Multi-Task Learning (MTL) to extract features related to attributes. Next Principal Component Analysis (PCA) reduce dimensionality of feature from CNN. Finally, Locality Sensitive Hashing (LSH) searches similar samples in the gallery. Extensive experiments were done on the clothing attribute dataset, experimental results proves this framework is effective.",
keywords = "Convolutional Neural Network (CNN), Fashion Description, Fashion Retrieval, Multi-Task Learning (MTL)",
author = "Yizhang Xia and Baitong Chen and Wenjin Lu and Frans Coenen and Bailing Zhang",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017 ; Conference date: 29-07-2017 Through 31-07-2017",
year = "2018",
month = jun,
day = "21",
doi = "10.1109/FSKD.2017.8393378",
language = "English",
series = "ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "804--808",
editor = "Liang Zhao and Lipo Wang and Guoyong Cai and Kenli Li and Yong Liu and Guoqing Xiao",
booktitle = "ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery",
}