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Título
Automated Diatom Classification (Part A): Handcrafted Feature Approaches
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Applied Sciences
Número de la revista
8
Cita Bibliográfica
Bueno, G., Deniz, O., Pedraza, A., Ruiz-Santaquiteria, J., Salido, J., Cristóbal, G., Borrego-Ramos, M., & Blanco, S. (2017). Automated diatom classification (Part A): handcrafted feature approaches. Applied Sciences, 7(8) Article e753. https://doi.org/10.3390/APP7080753
Editorial
MDPI
Fecha
2017
Resumen
[EN] 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
Materia
Palabras clave
Peer review
SI
ID proyecto
- info:eu-repo/grantAgreement/MINECO/Programa Estatal de I+D+I Orientada a los Retos de la Sociedad/CTM2014-51907-C2-R/ES/Desarrollo de métodos automáticos de identificación de diatomeas en el análisis cuantitativo y monitorización de la calidad de agua/AQUALITAS
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