@inproceedings{befdec8fe36f4e6b8147b065be823565,
title = "Improved convolutional neural network based scene classification using long short-term memory and label relations",
abstract = "Convolutional neural network (CNN) is more and more important in pattern recognition. In this work, we adopt label relations and long short-term memory (LSTM) to develop an accurate CNN-based scene classification algorithm. Traditional scene classification algorithms assume that labels are mutually exclusive. However, this is not reasonable when an image has a variety of objects and hence has multiple labels. In this work, we apply two label relations, which are exclusive and hierarchy relations, to improve the accuracy of multiple-label scene classification. For example, it is impossible that an image has both the labels of 'factory' and 'garden'. If the label 'factory' is assigned to an image, the probability that it has the label of 'garden' should be lowered. We also use image captioning to construct a scene classification model and propose an LSTM based method to further explore label relations and obtain more accurate results for scenic image labeling.",
keywords = "Convolutional neural network, long short-term memory, machine learning, pattern recognition, scene classification",
author = "Chen, {Po Jen} and Ding, {Jian Jiun} and Hsu, {Hung Wei} and Wang, {Chien Yao} and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017 ; Conference date: 10-07-2017 Through 14-07-2017",
year = "2017",
month = sep,
day = "5",
doi = "10.1109/ICMEW.2017.8026239",
language = "???core.languages.en_GB???",
series = "2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "429--434",
booktitle = "2017 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2017",
}