Half-against-half structure in classification of benthic macroinvertebrate images

Abstract

Benthic macroinvertebrates play a key role when water quality assessments are made. Benthic macroinvertebrates are difficult to identify and their identification need special expertise. Furthermore, manual identification is slow and expensive process. This paper concerns benthic macroinverte-brate classification when Half-Against-Half (HAH) structure was applied to Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Minimum Mahalanobis Distance Classifier (MMDC) classifiers. Especially, LDA, QDA and MMDC classifiers were for first time applied with HAH structure to benthic macroinvertebrate classification. We performed thorough experiments altogether with ten methods. In the case of HAH-SVM we managed to improve classification results from the earlier research by using a different approach to class division problem. We obtained 96.1% classification accuracy with Radial Basis Function (RBF) kernel. Moreover, new variants of LDA, QDA and MMDC classification methods achieved 89.5% and 91.6% classification accuracies which can be considered as a good result in such a difficult classification task.