Abstract
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.
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Zhan, H., Tu, X., Lyu, S., Lu, Y. (2020). Image Orientation Detection Using Convolutional Neural Network. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_46
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