Automated Diatom Classification (Part A): Handcrafted Feature Approaches
This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for wate...
| Autores: | , , , , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2017 |
| País: | España |
| Recursos: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/45802 |
| Acesso em linha: | https://doi.org/10.3390/app7080753 https://www.mdpi.com/2076-3417/7/8/753 https://hdl.handle.net/10578/45802 |
| Access Level: | acceso abierto |
| Palavra-chave: | automatic classification diatoms feature analysis handcrafted approaches morphological features textural features |
| Resumo: | This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future work. |
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