Open AccessClassification of Computer Graphic Images and Photographic Images Based on Fusion of Color and Texture Features
Author(s) -
Halaguru Basavarajappa Basanth Kumar,
H. R. Chennamma
Publication year - 2021
Publication title -
revue d'intelligence artificielle
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.146
H-Index - 14
eISSN - 1958-5748
pISSN - 0992-499X
DOI - 10.18280/ria.350303
Subject(s) - artificial intelligence , computer vision , computer science , pattern recognition (psychology) , rendering (computer graphics) , texture (cosmology) , local binary patterns , feature (linguistics) , fusion , gray level , image texture , digital image , pixel , image processing , image (mathematics) , histogram , linguistics , philosophy
With the rapid advancement in digital image rendering techniques, allows the user to create surrealistic computer graphic (CG) images which are hard to distinguish from photographs captured by digital cameras. In this paper, classification of CG images and photographic (PG) images based on fusion of global features is presented. Color and texture of an image represents global features. Texture feature descriptors such as gray level co-occurrence matrix (GLCM) and local binary pattern (LBP) are considered. Different combinations of these global features are investigated on various datasets. Experimental results show that, fusion of color and texture features subset can achieve best classification results over other feature combinations.