Classification of Computer Graphic and Photographic Images Using Convolution Neural Network

Authors

  • Ms. S. Sujitha  PG Student, Department of CSE, VV College of Engineering, Tirunelveli, Tamil Nadu, India
  • Dr. I. Muthulakshmi  Assistant Professor, Department of CSE, VV College of Engineering, Tirunelveli, Tamil Nadu, India

Keywords:

computergraphics (CG), photographic images (PG),convolutional neural network(CNN),image forensics.

Abstract

With the tremendous development of computer graphic rendering technology, photorealistic computer graphic images are difficult to differentiate from photo graphic images. In this project, a method is proposed based on Maximum Likelihood Principle Component Analysis (MLPCA) image features to distinguish computer graphic from photo graphic images using the CNN classifier. Initially the color image is transform dimension into 128X128 and then converted into gray scale image .The grayscale image can given into a convolution layer has filter or mask operation can performed .The filtered image can be given into ReLU layer. ReLU layer changes the all negative actions to Zero. Maximum Likelihood Principle Component Analysis(MLPCA) can perform feature extraction and reduce the dimensionality of the image .Fully connected layer which are used to generate new features from the existing features.Softmax layer is a classification layer it can be used to classify the computergraphic images from photographic images. Experimental results using Columbia database show that the method achieves reasonable detection accuracy.

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International Journal of Scientific Research in Science and Technology

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Published

2021-04-10

Issue

Volume 9, Issue 1, March-April-2021

Section

Research Articles

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How to Cite

[1]
Ms. S. Sujitha, Dr. I. Muthulakshmi, " Classification of Computer Graphic and Photographic Images Using Convolution Neural Network, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 1, pp.70-80, March-April-2021.