Feature Extraction and Classification Using Deep Convolutional Neural Networks
Keywords:Convolutional neural network, Max pooling, Average pooling, Subsampling, parameter sharing, local connectivity
The impressive gain in performance obtained using deep neural networks (DNN) for various tasks encouraged us to apply DNN for image classification task. We have used a variant of DNN called Deep convolutional Neural Networks (DCNN) for feature extraction and image classification. Neural networks can be used for classification as well as for feature extraction. Our whole work can be better seen as two different tasks. In the first task, DCNN is used for feature extraction and classification task. In the second task, features are extracted using DCNN and then SVM, a shallow classifier, is used to classify the extracted features. Performance of these tasks is compared. Various configurations ofDCNNare used for our experimental studies.Among different architectures that we have considered, the architecture with 3 levels of convolutional and pooling layers, followed by a fully connected output layer is used for feature extraction. In task 1 DCNN extracted features are fed to a 2 hidden layer neural network for classification. In task 2 SVM is used to classify the features extracted by DCNN. Experimental studies show that the performance of υ-SVM classification on DCNN features is slightly better than the results of neural network classification on DCNN extracted features.
Jyostna Devi Bodapati and N. Veeranjaneyulu, “Abnormal Network Traffic Detection Using Support Vector Data Description”, Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications. Springer, Singapore, 2017.
Jyostna Devi Bodapati and N. Veeranjaneyulu, “Performance of different Classifiers in non-linear subspace” Proceedings of the International Conference on Signal and Information Processing, 2016.
Veeranjaneyulu N and Jyostna devi Bodapati, “Scene classification using support vector machines with LDA”, Journal of theoretical and applied information technology, Vol. 63, pp. 741, 2014.
Tara N. Sainath, Abdel-rahman Mohamed, Nrian Kingsbury, Bhuvana Ramabhadram, “Deep convolutional neural networks for LVCSR”, ICASSP, 2013.
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, “Image Net Classification with Deep Convolutional Neural Networks”, NIPS, 2012.
DH Hubel and TN Wiesel, “Receptive fields of single neurones in the cat’s striate cortex”, The Journal of Physiology, Vol. 3, pp. 574–579, 1959.
LeCun, Yann, et al. “Gradient-based learning applied to document recognition”, Proceedings of the IEEE 86.11, pp. 2278–2324, 1998.
Scherer, Dominik, Andreas Mller, and Sven Behnke, “Evaluation of pooling operations in Convolutional architectures for object recognition”, International Conference on Artificial Neural Networks, Springer Berlin Heidelberg, pp. 92–101, 2010.
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton, “Imagenet classiffication with deep convolutional neural networks”, Advances in neural information processing systems, 2012.
Simon Haykin, “Neural Network and Learning Machines”, McMaster University Hamilton, Ontario, Canada.
Zeiler, Matthew D., and Rob Fergus, “Stochastic pooling for regularization of deep convolutional neural networks”. arXiv preprint arXiv:1301.3557, 2013.
Ikbal, M. Shajith, Hemant Misra, and Bayya Yegnanarayana, “Analysis of autoassociative mapping neural networks”, International Joint Conference Neural Networks, Vol. 5. IEEE, 1999.
Jyostna devi Bodapati & N Veeranjaneyulu, “An Intelligent face recognition system using Wavelet Fusion of K-PCA, R-LDA”, ICCCCT, pp. 437–441, 2010.
Ciregan, Dan, Ueli Meier, and Jürgen Schmidhuber. “Multi-column deep neural networks for image classification”. Computer vision and pattern recognition (CVPR), 2012 IEEE conference on. IEEE, 2012.
Esteva, Andre, et al. “Dermatologist-level classification of skin cancer with deep neural networks”. Nature 542.7639, 2015.
Courbariaux, Matthieu, Yoshua Bengio, and Jean-Pierre David. “Binaryconnect: Training deep neural networks with binary weights during propagations”. Advances in neural information processing systems, 2015.
Cortes, Corinna, et al. “Adanet: Adaptive structural learning of artificial neural networks”. arXiv preprint arXiv:1607.01097, 2016.
Shin, Hoo-Chang, et al. “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning”. IEEE transactions on medical imaging, 35.5, pp. 1285–1298, 2016.
Toshev, Alexander, and Christian Szegedy. “Deeppose: Human pose estimation via deep neural networks”. Proceedings of the IEEE conference on computer vision and pattern recognition, 2014.
Szegedy, Christian, Dumitru Erhan, and Alexander Toshkov Toshev. “Object detection using deep neural networks”. U.S. Patent No. 9,275,308, 2016.
Neethu Narayanan, K. Suthendran and Fepslin AthishMon, “Recognizing Spontaneous Emotion From The Eye Region Under Different Head Poses”, International Journal of Pure and Applied Mathematics, Vol. 118, pp. 257–263, 2018.