Classification of Highly Similar Fish Species Using Machine Learning

Fish species inventory and monitoring is a timely concern due to the increasing environmental imbalance. In this sense, the development of machine learning strategies aiming to support automatic fish species identification is an important contribution, both in environmental and scientific terms. This study explores handcrafted features aiming to perform fish species identification in a quite challenging scenario, in which the inter-classes differences are minimal, as the species belong to the same family considering the hierarchy of biological classification (i.e. Characidae). For this purpose, it is proposed a set of features designed properly, aiming to capture visual properties closely related to each species that are difficult to be noted by experts in this domain of investigation. An additional contribution of this work is a dataset of 4870 images taken from 416 individuals from nine fish species. The Region of Interest (RoI) corresponding to the fish body is segmented, and horizontal and vertical histograms are collected from each sample, aiming to capture information related to the body shape, and other characteristics particular to each species. Experiments are performed using the Support Vector Machine (SVM), K-Nearest Neighbours (K-NN), Random Forest and Naive-Bayes classifiers and, in the best scenario, it is obtained an accuracy rate of 99%. This result corroborates the hypothesis that the use of handcrafted features specifically developed for this type of application can be quite effective in addressing this classification problem.