@inproceedings{ae1568213f3d4bfc843dc7ce4e00e559,
title = "Inception classification and object detection based joint-cnn for indoor scene classification",
abstract = "While convolutional neural network (CNN) has been successfully used in many fields including single-label scene classification, it is vital to note that real world scenes generally contain multiple semantics and multi-label, especially in the indoor scene classification due to its content complexity. At the same time, most approaches try to make the network much deeper to make sure that they can extract more detail information. However, the deeper network will cause a lot of problems such as the increase of computational costs and network costs and so on. In order to solve these problems, this paper presents a novel framework which called Joint-CNN based on the proposed special label extraction and network structure. Extensive experiments on various data sets show that our method has enhanced the performance on MIT indoor67 and SUN397 data sets.",
keywords = "CNN, Indoor scene, Multi-label, Scene classification",
author = "Yanling Tian and Weitong Zhang and Qieshi Zhang and Gang Lu",
note = "Publisher Copyright: {\textcopyright} 2018 Newswood Limited. All rights reserved. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018 ; Conference date: 14-03-2018 Through 16-03-2018",
year = "2018",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "334--338",
editor = "Ao, {S. I.} and Craig Douglas and Feng, {David Dagan} and Korsunsky Korsunsky and Oscar Castillo",
booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018",
}