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http://dx.doi.org/10.6109/jkiice.2017.21.1.144

Training Network Design Based on Convolution Neural Network for Object Classification in few class problem  

Lim, Su-chang (Department of Computer Science, Sunchon National University)
Kim, Seung-Hyun (Department of Computer Science, Sunchon National University)
Kim, Yeon-Ho (Department of Computer Science, Sunchon National University)
Kim, Do-yeon (Department of Computer Engineering, Sunchon National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.1, 2017 , pp. 144-150 More about this Journal
Abstract
Recently, deep learning is used for intelligent processing and accuracy improvement of data. It is formed calculation model composed of multi data processing layer that train the data representation through an abstraction of the various levels. A category of deep learning, convolution neural network is utilized in various research fields, which are human pose estimation, face recognition, image classification, speech recognition. When using the deep layer and lots of class, CNN that show a good performance on image classification obtain higher classification rate but occur the overfitting problem, when using a few data. So, we design the training network based on convolution neural network and trained our image data set for object classification in few class problem. The experiment show the higher classification rate of 7.06% in average than the previous networks designed to classify the object in 1000 class problem.
Keywords
Deep learning; Neural Network; Object Classification; Convolution Neural Network;
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1 Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no.7553, pp. 436-444, May 2015.   DOI
2 M. D. Zeiler, and R. Fergus, "Visualizing and understanding convolutional networks," in Proceedings of the 13th European Conference on Computer Vision, Zurich: CH, pp. 818-833, 2014.
3 O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, and A.C. Berg, "ImageNet large scale visual recognition challenge," International Journal of Computer Vision, vol. 115, no. 3, pp. 211-252, Dec. 2015.   DOI
4 K. Alex, S. Ilya, and H. Geoffrey, "ImageNet classification with deep convolutional neural networks," in Proceedings of Advances in Neural Information Processing System, Nevada: NV, pp. 1097-1105, 2012.
5 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston: MA, pp. 1-9, 2015.
6 S. Karen and Z. Andrew, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
7 J.A.K. Suykens, and J. Vandewalle, "Least squares support vector machine classifiers," Neural processing letters 9, no.3, pp.293-300, 1999.   DOI
8 R. Gunnar, T. Onoda, and K. Muller, "Soft margins for adaBoost," Machine learning, vol. 42, no. 3, pp.287-320. 2001.   DOI
9 Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the Institute of Electrical and Electronics Engineers, vol. 86, no.11, pp.2278-2324, 1998.
10 Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, "Caffe: An open source convolutional architecture for fast feature embedding," in Proceedings of the 22nd ACM international conference on Multimedia, pp.675-678, 2014.