Behavior of SVM based classification for varying sizes of heap-grain images
DOI:
https://doi.org/10.26438/ijcse/v6i12.3242Keywords:
Classification, feature extraction, grain samples, support vector machineAbstract
This paper describes the behavior of support vector machine based classification for varying sizes of heap-grain samples. Different grains like cow peas, green gram, ground nut, green peas, jowar, red gram, soya and toor dal are considered for the study. The color and texture features are used as input to the SVM classifier. The recognition accuracy is observed for specific size training and mixed size training methods. The recognition accuracy is found to be 100% for the test samples with which the classifier is trained and decreased when training and testing samples are different. The work finds application in automatic recognition and classification of food grains by the service robots in the real world.
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