2020
Díez-Pastor, José Francisco; Latorre-Carmona, Pedro; Arnaiz-González, Álvar; Ruiz-Pérez, Javier; Zurro, Débora
“You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification Journal Article
In: Microscopy and Microanalysis, vol. 26, pp. 1158-1167, 2020, ISSN: 1431-9276.
Abstract | Links | BibTeX | Tags: Feature extraction, Machine learning, Microfossils, Morphometry, Proxy
@article{Díez-Pastor2020,
title = {“You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification},
author = {José Francisco Díez-Pastor and Pedro Latorre-Carmona and Álvar Arnaiz-González and Javier Ruiz-Pérez and Débora Zurro},
url = {https://www.cambridge.org/core/journals/microscopy-and-microanalysis/article/you-are-not-my-type-an-evaluation-of-classification-methods-for-automatic-phytolith-identification/48F88E9407086B797BBE383B8BC15904},
doi = {https://doi.org/10.1017/S1431927620024629},
issn = {1431-9276},
year = {2020},
date = {2020-11-10},
journal = {Microscopy and Microanalysis},
volume = {26},
pages = {1158-1167},
abstract = {Phytoliths can be an important source of information related to environmental and climatic change, as well as to ancient plant use by humans, particularly within the disciplines of paleoecology and archaeology. Currently, phytolith identification and categorization is performed manually by researchers, a time-consuming task liable to misclassifications. The automated classification of phytoliths would allow the standardization of identification processes, avoiding possible biases related to the classification capability of researchers. This paper presents a comparative analysis of six classification methods, using digitized microscopic images to examine the efficacy of different quantitative approaches for characterizing phytoliths. A comprehensive experiment performed on images of 429 phytoliths demonstrated that the automatic phytolith classification is a promising area of research that will help researchers to invest time more efficiently and improve their recognition accuracy rate.},
keywords = {Feature extraction, Machine learning, Microfossils, Morphometry, Proxy},
pubstate = {published},
tppubtype = {article}
}
Phytoliths can be an important source of information related to environmental and climatic change, as well as to ancient plant use by humans, particularly within the disciplines of paleoecology and archaeology. Currently, phytolith identification and categorization is performed manually by researchers, a time-consuming task liable to misclassifications. The automated classification of phytoliths would allow the standardization of identification processes, avoiding possible biases related to the classification capability of researchers. This paper presents a comparative analysis of six classification methods, using digitized microscopic images to examine the efficacy of different quantitative approaches for characterizing phytoliths. A comprehensive experiment performed on images of 429 phytoliths demonstrated that the automatic phytolith classification is a promising area of research that will help researchers to invest time more efficiently and improve their recognition accuracy rate.