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Microalgae Classification using Semi-supervised and Active Learning based on Gaussian Mixture Models
Abstract and Figures
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 of microalgae based on semi-supervised and active learning algorithms, using Gaussian mixture models. The results show that the approach has an excellent cost-benefit relation, classifying more than 90 % of microalgae in a well distributed way, overcoming the supervised algorithm SVM.
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