Fish Species Classification using SVM Kernels

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 %.