Abstract
Convolution Neural Network (CNN) is one of the most popular deep learning methods in recent years, which achieves great success in the field of image classification. In this paper, an improved image classification method considering rotation based on CNN is proposed. Essentially, convolution is only a method to smooth the image, which doesn’t consider the effect of image rotation any more. It can be proven that after some images are rotated \(180^{\circ }\), CNN can recognize them well while fail to recognize them before. So, rotation is one of the efficient ways to improve object recognization. Four kinds of typical CNN are adopted in this paper, which are CaffeNet, VGG16, VGG19 and GoolgeNet. It has been proven that the accurate rates are all increased no matter which one is adopted among these four CNN. This method proposed in this paper can recognize dangerous objects automatically with good performances.
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The work is supported by National Natural Science Foundation of China (No. 11402294, No. 11502062) and Open Fund of Tianjin Key Lab for Advanced Signal Processing (No. 2015AFS03).
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Qu, J. (2016). An Improved Image Classification Method Considering Rotation Based on Convolutional Neural Network. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_36
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DOI: https://doi.org/10.1007/978-3-319-42553-5_36
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