Open Access   Article

Behavior of SVM based classification for varying sizes of heap-grain images

Vishwanath S. Kamatar1 , Rajesh Yakkundimath2 , Girish Saunshi3

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-12 , Page no. 32-42, Dec-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i12.3242

Online published on Dec 31, 2018

Copyright Vishwanath S. Kamatar, Rajesh Yakkundimath, Girish Saunshi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: Vishwanath S. Kamatar, Rajesh Yakkundimath, Girish Saunshi, Behavior of SVM based classification for varying sizes of heap-grain images, International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.32-42, 2018.

MLA Style Citation: Vishwanath S. Kamatar, Rajesh Yakkundimath, Girish Saunshi "Behavior of SVM based classification for varying sizes of heap-grain images." International Journal of Computer Sciences and Engineering 6.12 (2018): 32-42.

APA Style Citation: Vishwanath S. Kamatar, Rajesh Yakkundimath, Girish Saunshi, (2018). Behavior of SVM based classification for varying sizes of heap-grain images. International Journal of Computer Sciences and Engineering, 6(12), 32-42.

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Abstract

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.

Key-Words / Index Term

Classification, feature extraction, grain samples, support vector machine

References

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