Pandiyan P, Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, India
Rajasekaran T, Vishnu Kumar K, Sivaramakrishnan R and Thigarajan T, Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, India
Online First : 30 December 2020
Publisher Name : IJAICT India Publications, India.
Print ISBN : 978-81-950008-0-7
Online ISBN : 978-81-950008-1-4
Page :466-470
Abstract
This paper presents classification of fish species using support vector machine (SVM) algorithm with four kernel functions such as linear, polynomial, sigmoid and radial basis functions. The datasets for performing this research is obtained from Fish-Pak website which has required number of images for classifying the two different fish species namely Catla and Rohu with three fish features like head, body and scale data. The number of images for Rohu fish species is not equal to the Catla type fish species therefore image augmentation technique is used to balance the number of images. The simulation results reveal that SVM with radial basis function-based kernel provides the accuracy of 78 %.
Keywords
Deep Learning, SVM, Fish classification, RBF Kernel.
Cite this article
Pandiyan P,Rajasekaran T, Vishnu Kumar K, Sivaramakrishnan R and Thigarajan T, “Fish Species Classification using SVM Kernels”, Innovations in Information and Communication Technology, pp.466-470, December 2020.
Copyright
© 2020 Pandiyan P,Rajasekaran T, Vishnu Kumar K, Sivaramakrishnan R and Thigarajan T. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
