TY - GEN
T1 - Image Orientation Detection Using Convolutional Neural Network
AU - Zhan, Hongjian
AU - Tu, Xiao
AU - Lyu, Shujing
AU - Lu, Yue
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Image orientation detection is often a prerequisite for many applications of image understanding and recognition. Recently, with the development of deep learning, mang significant Convolutional Neural Network (CNN) architectures are proposed and widely used in computer vision areas. In order to investigate the performance of CNN on image orientation detection task, in this paper, we first evaluate several famous CNN architectures, such as AlexNet, GoogleNet and VGGNet on this task, then we test a new CNN architecture by combining these networks. We collect six kinds of image, including landscape, block, indoor, human face, mail and natural images, in which the first three ones are regarded as difficult categories of orientation detection by previous work. The experiment results on these datasets indicate the effectiveness of the proposed network on image orientation detection task.
AB - Image orientation detection is often a prerequisite for many applications of image understanding and recognition. Recently, with the development of deep learning, mang significant Convolutional Neural Network (CNN) architectures are proposed and widely used in computer vision areas. In order to investigate the performance of CNN on image orientation detection task, in this paper, we first evaluate several famous CNN architectures, such as AlexNet, GoogleNet and VGGNet on this task, then we test a new CNN architecture by combining these networks. We collect six kinds of image, including landscape, block, indoor, human face, mail and natural images, in which the first three ones are regarded as difficult categories of orientation detection by previous work. The experiment results on these datasets indicate the effectiveness of the proposed network on image orientation detection task.
KW - Convolutional neural network
KW - Deep learning
KW - Orientation detection
UR - https://www.scopus.com/pages/publications/85092902490
U2 - 10.1007/978-3-030-59830-3_46
DO - 10.1007/978-3-030-59830-3_46
M3 - 会议稿件
AN - SCOPUS:85092902490
SN - 9783030598297
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 538
EP - 546
BT - Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings
A2 - Lu, Yue
A2 - Vincent, Nicole
A2 - Yuen, Pong Chi
A2 - Zheng, Wei-Shi
A2 - Cheriet, Farida
A2 - Suen, Ching Y.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020
Y2 - 19 October 2020 through 23 October 2020
ER -