Abstract

JANY ARMAN, Rafsun; HOSSAIN, Monowar  and  HOSSAIN, Sabir. Fish Classification using Saliency Detection Depending on Shape and Texture. Comp. y Sist. [online]. 2022, vol.26, n.1, pp.303-310.  Epub Aug 08, 2022. ISSN 2007-9737.  https://doi.org/10.13053/cys-26-1-4174.

Classification of fishes becomes important after the advancement of machine learning. As fishes play a vital role in the economy of Bangladesh, a proper monitoring system will maximize the cultivation. It will also contribute to the overall economy. Therefore, here introduce a system that can detect the fishes and compare various methods with explanations to understand the selected methods. This paper have considered 5 categories of local fishes of Bangladesh in the dataset. The technique consists of preprocessing with segmentation, feature descriptor, and ensembles to produce the final result. U2-net is used in the preprocessing layer to obtain two types of features namely shaped images and colored images with removed backgrounds. To get the features, we have used a histogram of oriented gradient (HOG) and an ensemble layer is used for classification purposes. Experimental results illustrate the accuracy of 99.77% for the first ensemble and 100% for the second ensemble layer on our dataset of 2678 fishes of 5 distinguishing classes. Various layers were used to compare the predicted results using different performance metrics.

Keywords : U²-net; hog; knn; SVM; logistic regression; decision tree; fish classification; segmentation; salient object detection.