Automated Diatom Classification (Part A): Handcrafted Feature Approaches

Bueno, Gloria; Deniz, Oscar; Pedraza, Anibal; Ruiz-Santaquiteria, Jesús; Salido, Jesús; Cristóbal, Gabriel; Borrego-Ramos, María; Blanco, Saúl

doi:10.3390/app7080753

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessFeature PaperArticle

Automated Diatom Classification (Part A): Handcrafted Feature Approaches

Gloria Bueno

^1,*

Oscar Deniz

Anibal Pedraza

Jesús Ruiz-Santaquiteria

¹,

and

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.

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)

View Full-Text Download PDF

Browse Figures

Citation Export

Abstract

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

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Share and Cite

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. https://doi.org/10.3390/app7080753

AMA 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. Applied Sciences. 2017; 7(8):753. https://doi.org/10.3390/app7080753

Chicago/Turabian Style

Bueno, Gloria, Oscar Deniz, Anibal Pedraza, Jesús Ruiz-Santaquiteria, Jesús Salido, Gabriel Cristóbal, María Borrego-Ramos, and Saúl Blanco. 2017. "Automated Diatom Classification (Part A): Handcrafted Feature Approaches" Applied Sciences 7, no. 8: 753. https://doi.org/10.3390/app7080753

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

Automated Diatom Classification (Part A): Handcrafted Feature Approaches

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI