Automated Diatom Classification (Part A): Handcrafted Feature Approaches
by
Gloria Bueno 1,*
, Oscar Deniz 1, Anibal Pedraza 1, Jesús Ruiz-Santaquiteria 1, Jesús Salido 1, Gabriel Cristóbal 2, María Borrego-Ramos 3 and Saúl Blanco 3
Gloria Bueno 1,*, Oscar Deniz 1, Anibal Pedraza 1, Jesús Ruiz-Santaquiteria 1, Jesús Salido 1, Gabriel Cristóbal 2, María Borrego-Ramos 3 and Saúl Blanco 3
1
VISILAB-University of Castilla-La Mancha, Av. Camilo José Cela s/n, 13071 Ciudad Real, Spain
2
Institute of Optics, Spanish National Research Council (CSIC), Serrano 121, 28006 Madrid, Spain
3
The Institute of the Environment, University of Leon, E-24071 León, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2017, 7(8), 753; https://doi.org/10.3390/app7080753
Received: 31 May 2017 / Revised: 11 July 2017 / Accepted: 18 July 2017 / Published: 25 July 2017
(This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images)
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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Keywords:
feature analysis; textural features; morphological features; automatic classification; handcrafted approaches; diatoms
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MDPI and ACS Style
Bueno, G.; Deniz, O.; Pedraza, A.; Ruiz-Santaquiteria, J.; Salido, J.; Cristóbal, G.; Borrego-Ramos, M.; Blanco, S. Automated Diatom Classification (Part A): Handcrafted Feature Approaches. Appl. Sci. 2017, 7, 753.
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