Computer Graphic and Photographic Image Classification using Local Image Descriptors

Author:

Birajdar Gajanan KORCID,Mankar Vijay H

Abstract

<p class="p1">With the tremendous development of computer graphic rendering technology, photorealistic computer graphic images are difficult to differentiate from photo graphic images. In this article, a method is proposed based on discrete wavelet transform based binary statistical image features to distinguish computer graphic from photo graphic images using the support vector machine classifier. Textural descriptors extracted using binary statistical image features are different for computer graphic and photo graphic which are based on learning of natural image statistic filters. Input RGB image is first converted into grayscale and decomposed into sub-bands using Haar discrete wavelet transform and then binary statistical image features are extracted. Fuzzy entropy based feature subset selection is employed to choose relevant features. Experimental results using Columbia database show that the method achieves good detection accuracy.</p>

Publisher

Defence Scientific Information and Documentation Centre

Subject

Electrical and Electronic Engineering,Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Biomedical Engineering,General Chemical Engineering

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2. A Systematic Survey on Photorealistic Computer Graphic and Photographic Image Discrimination;International Journal of Image and Graphics;2022-07-05

3. Evaluation of Food Photography in the Context of Graphic Design;Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi;2022-06-02

4. An Improved Method Research on Graphics and Image Processing System;Security and Communication Networks;2022-03-28

5. Colour-Range Histogram technique for Automatic Image Source Detection;Informatica;2020-06-15

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