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

Brazilian Computer Society. Journal

Amá¹  Paulo Drews-²  Virgí²  Pablo Machado³  Matheus Faria⁵  Rafael G. Colares⁶  nia Tavano⁶  lia Detoni⁷ 

[1] ãCentro de CiêDepartamento de CiêInstituto de Oceanografia, Universidade Federal do Rio Grande (FURG), Rio Grande, Brazil;ncia da Computaçncias Computacionais, Universidade Federal do Rio Grande (FURG), Rio Grande, Brazil;o, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil

关键词: Active learning;    Semi-supervised learning;    Microalgae classification;   

DOI  :  10.1007/s13173-013-0121-y

学科分类：农业科学（综合）

来源: Springer U K

【 摘 要 】

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.

【 授权许可】

CC BY