Indigenous Fish Classification of Bangladesh using Hybrid Features with SVM Classifier

Abstract

In Computer Vision, automatic processing system gaining its popularity for its powerful classification and detection ability. Indigenous fish is an important element in natural food system which established the main diet in rural households. Hence, the classification of indigenous fish plays a vital role in authentication, preservation, and production. In this paper, we introduce a Hybrid Local Binary Pattern (HLBP), an adaptive threshold based hybrid feature descriptor which extracts sign and magnitude from an image. Afterward, we use different kernels of SVM for classification.We have also created a new indigenous fish dataset namely BDIndigenousFish2019 which contains images of eight different Bangladeshi fish species. The experimental result on BDIndigenousFish2019. The proposed HLBP is implemented for the classification of some indigenous fish species of Bangladesh with different kernels of SVM classifier. This paper focuses on the classification of some indigenous fishes of Bangladesh by means of SVM classifier with different kernels. We have conducted the experiment on our own indigenous fish dataset and comparative analysis HLBP with some well-known feature descriptors such as LBP, LGP, NABP, CENTRIST, DTCTH and LAID. Therefore, we evaluate the experimental results and our proposed model gain higher accuracy of 90% than other methods.