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Appl. Sci. 2017, 7(8), 753;

Automated Diatom Classification (Part A): Handcrafted Feature Approaches

VISILAB-University of Castilla-La Mancha, Av. Camilo José Cela s/n, 13071 Ciudad Real, Spain
Institute of Optics, Spanish National Research Council (CSIC), Serrano 121, 28006 Madrid, Spain
The Institute of the Environment, University of Leon, E-24071 León, Spain
Author to whom correspondence should be addressed.
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)
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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. View Full-Text
Keywords: feature analysis; textural features; morphological features; automatic classification; handcrafted approaches; diatoms feature analysis; textural features; morphological features; automatic classification; handcrafted approaches; diatoms

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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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