Feature Extraction and Classification Using Deep Convolutional Neural Networks

Authors

  • Jyostna Devi Bodapati Assistant Professor, Department of CSE, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Andhra Pradesh, India
  • N. Veeranjaneyulu Professor, Department of IT, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Andhra Pradesh, India

DOI:

https://doi.org/10.13052/2245-1439.825

Keywords:

Convolutional neural network, Max pooling, Average pooling, Subsampling, parameter sharing, local connectivity

Abstract

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.

 

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Author Biographies

Jyostna Devi Bodapati, Assistant Professor, Department of CSE, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Andhra Pradesh, India

Jyostna Devi Bodapati working as Assistant Professor in the Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, India since 2010. She has published good number of research articles in reputed International journals and Conferences. Her areas of interests are Deep learning, Pattern Recognition and Kernel methods for pattern analysis.

N. Veeranjaneyulu, Professor, Department of IT, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Andhra Pradesh, India

N. Veeranjaneyulu is a Professor at Vignan’s Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, India. He has 21 years of teaching experience in the field of Computer Science. He has around 50 publications in International journals and Conference proceedings. His areas of interests are cloud computing and Big-data Analytics.

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Published

2018-01-17

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