Abstract
Image classification is one of the most important problems for computer vision and machine learning. Many image classification methods have been proposed and applied to many application areas. But how to improve the performance of image classification is still an important research issue to be resolved. Feature extraction is the most important task of image classification, which affects the classification performance directly. Classical features extraction methods are designed manually according to color, shape or texture etc. They can only display the image characters partially and can’t be extracted objectively. Convolutional Neural Network (CNN), which is one kind of artificial neural networks, has already become current research focuses for image classification. Deep learning based on CNN can extract image features automatically. For improving image classification performance, a novel image classification method that combines CNN and parallel SVM is proposed. In the method, deep neural network based on CNN is used to extract image features. Extracted features are input to a parallel SVM based on MapReduce for image classification. It can improve the classification accuracy and efficiency markedly. The efficiency of the proposed method is illustrated through examples analysis.
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Acknowledgements
This work is supported by the national science foundation (No. 61472230), National Natural Science Foundation of China (Grant No. 61402271), Shandong science and technology development plan (Grant No. 2016GGC01061, 2016GGX101029, J15LN54), Director Funding of Shandong Provincial Key Laboratory of computer networks.
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Sun, Z., Li, F., Huang, H. (2017). Large Scale Image Classification Based on CNN and Parallel SVM. In: Liu, D., **e, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_57
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DOI: https://doi.org/10.1007/978-3-319-70087-8_57
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