Microalgae classification using semi-supervised and active learning based on Gaussian mixture models

Abstract

Microalgae are unicellular organisms that have different shapes, sizes and structures. Classifying these microalgae manually can be an expensive task, because thousands of microalgae can be found in even a small sample of water. This paper presents an approach for an automatic/semi-automatic classification ofmicroalgae based on semi-supervised and active learning algorithms, using Gaussian mixturemodels. The results showthat the approach has an excellent cost-benefit relation, classifying more than 90 % of microalgae in a well distributed way, overcoming the supervised algorithm SVM.