Authors:
Ahmed Kharrat
1
;
Nacéra Benamrane
1
;
Mohamed Ben Messaoud
2
and
Mohamed Abid
2
Affiliations:
1
University of Sfax, Faculty of Science and Vision and Medical Imagery Laboratory U.S.T.O., Tunisia
;
2
University of Sfax, Tunisia
Keyword(s):
Support vector machine, Classification, Genetic algorithm, Parameters optimisation, Feature selection.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Early Vision and Image Representation
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Segmentation and Grouping
;
Signal Processing, Sensors, Systems Modeling and Control
;
Statistical Approach
;
Wavelet Analysis
Abstract:
The parameter selection is very important for successful modelling of input–output relationship in a function classification model. In this study, support vector machine (SVM) has been used as a function classification tool for accurate segregation and genetic algorithm (GA) has been utilised for optimisation of the parameters of the SVM model. Having as input only five selected features, parameters optimisation for SVM is applied. The five selected features are mean of contrast, mean of homogeneity, mean of sum average, mean of sum variance and range of autocorrelation. The performance of the proposed model has been compared with a statistical approach. Despite the fact that Grid algorithm has fewer processing time, it does not seem to be efficient. Testing results show that the proposed GA–SVM model outperforms the statistical approach in terms of accuracy and computational efficiency.