2026-06-30T01:13:12Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/73782023-03-22T12:05:54Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

Diez Pastor, José Francisco Latorre Carmona, Pedro Arnaiz González, Álvar Ruiz Pérez, Javier Zurro, Débora 2023-02-06T07:40:24Z 2023-02-06T07:40:24Z 2020-11 1431-9276 http://hdl.handle.net/10259/7378 10.1017/S1431927620024629 1435-8115 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. eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Feature extraction Machine learning Microfossils Morphometry Proxy “You Are Not My Type”: An Evaluation of Classification Methods for Automatic Phytolith Identification info:eu-repo/semantics/article