@Article{JFBI-11-209,
author = {Li , Qiao-QiZhong , Yue-Qi and Wang , Xin},
title = {Classification of Female Apparel using Convolutional Neural Network},
journal = {Journal of Fiber Bioengineering and Informatics},
year = {2019},
volume = {11},
number = {4},
pages = {209--216},
abstract = {<p>With the vigorous development of clothing e-commerce, the amount of clothing image data on the
internet has increased dramatically. A tedious effort was required to manually label and classify the
semantic attributes of clothing images. Manual marking is time-consuming and laborious, so a method
of automatic classification using convolutional neural networks was studied. In this paper, a female
cloth dataset consisting of 10 types of female clothing was built. Convolutional Neural Network (CNN)
was employed to learn the feature vectors for each type. Five different types of architectures, including
ResNet50, Inception-v3, and VGG-19, AlexNet, and FashionNet were used for performance comparison.
Experimental results have shown that Inception-v3 possesses the highest accuracy (98.07% for training
and 96.91% for testing) in clothing classification compared with other methods.</p>},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbim00319},
url = {http://global-sci.org/intro/article_detail/jfbi/13010.html}
}