| Two main steps in object recognition systems involve feature representation and classification. In this study, the combination of principal components analysis (PCA), linear discriminant analysis (LDA) and support vector machine (SVM) approaches were used to develop a cultivar classification system for Anthurium flower, which PCA, LDA, and SVM were applied for data reduction, feature extraction, and classification, respectively. The system was tested on a database of Anthurium flower images, which included the images of 20 cultivars of the flower with different sizes, angles of rotation placed under the imaging camera, and lighting conditions. Results were evaluated from the two point of view of classification accuracy and processing time, and the approach... |
