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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Gold Tree Sorting and Classification Using Support Vector Machine Classifier
verfasst von : R. S. Sabeenian, M. E. Paramasivam, Pon Selvan, Eldho Paul, P. M. Dinesh, T. Shanthi, K. Manju, R. Anand
Erschienen in: Advances in Machine Learning and Computational Intelligence
Verlag: Springer Singapore
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Abstract
Sorting and classification of child items from gold tree is a challenge over the years. The various subcomponents (cast) in a gold tree are known as child items. Recently, some algorithms have been proposed to sort child items in gold tree. The shortfalls of the existing methods are high misclassification of child items. To overcome the above challenges, SVM and GLCM-based approach is proposed. GLCM feature is extracted from the test and train images which are fed to the multiclass SVM classifier. The classification of image is done with the help of SVM classifier. For training and validation purpose, around 1000 images are used and labeled into 15 different classes. The proposed algorithm provides better quality metrics compared to other existing classification algorithms. The main application of proposed algorithm is in Gold and ornament industries to improves productivity.