Document Type
Article
Publication Date
6-12-2015
Journal Title
Journal of Advances in Mathematics and Computer Science
ISSN
2217-2236
Keywords
Zernike Moment, Feature Extraction, Geometric Properties, Image Classification
Disciplines
Agricultural Science | Agronomy and Crop Sciences | Applied Mathematics | Computer Sciences | Mathematics
Abstract
Aims/ objectives: To demontrate effectiveness of Zernike Moments for Image Classification. Zernike moment(ZM) is an excellent region-based moment which has attracted the attentions of many image processing researchers since its first application to image analysis. Many papers have been published on several works done on ZM but no single paper ever give a detailed information of how the computation of ZM is done from the time the image is captured to the computation of ZM. This work showed how to effectively apply ZM on RGB images. We have demonstrated the effectiveness of Zernike moment in image classification system. A neuro-genetic intelligent system has been built with PNN classifier. The feature extracted viz ZM and Geometric features were further subjected to GA to bring the best combinatorial features for optimal accuracy. The algebraic structure of our novel fitness function enabled the GA to select the best results. The 10-fold CV used enabled the whole system to be unbiased giving a classification accuracy of 90.05%. A demonstration of affine properties of ZM are comprehensively stated and explained. In summary, the ZM enabled the classifier to have improved accuracy of 91% as compared with Geometric features with 89% accuracy.
Recommended Citation
Oluleye, Babatunde, Armstrong Leisa, Jinsong Leng, and Diepeveen Dean. 2014. “Zernike Moments and Genetic Algorithm: Tutorial and Application”. Journal of Advances in Mathematics and Computer Science 4 (15):2217-36. https://doi.org/10.9734/BJMCS/2014/10931.
Included in
Agricultural Science Commons, Agronomy and Crop Sciences Commons, Applied Mathematics Commons, Computer Sciences Commons, Mathematics Commons
