Computer Graphic and Photographic Image Classification Using Transfer Learning Approach

Abstract

Advancement in information and communication technology has led to tremendous development in graphics techniques. Evolving multimedia tools are used to generate high quality Computer Graphics (CG) images. These images have wide applications in domains like video gaming, augmented reality, and virtual reality and many other. Computer graphic images are also used illegally in criminal activities. This article proposes an effective transfer learning approach to classify CG and Photographic (PG) images available in small scale dataset. Initially, pre-trained models such as AlexNet, GoogleNet, ResNet50, VGG-18 and SqueezeNet were modified and fine-tuned appropriately. Based on the validation accuracy, SqueezeNet was adapted as learning model for extracting deep features for classification. To evaluate the performance of squeezeNet, Columbia dataset and Photo realistic dataset were used. Finally, the performance of the proposed model was compared with state-of-the- art transfer learning approaches to prove its efficacy. Accuracy of 93.75% was attained using SqueezeNet for the folding ratio 80:20 when the input data is augmented.