2026-06-21T00:31:36Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/112352026-01-17T01:05:29Zcom_10259_5377com_10259_5086com_10259_2604col_10259_5378

Diez Pastor, José Francisco Latorre Carmona, Pedro Arnaiz González, Álvar Canepa Oneto, Antonio Jesús Ruiz Pérez, Javier Zurro, Débora 2026-01-16T12:38:39Z 2026-01-16T12:38:39Z 2024-07 2192-6352 https://hdl.handle.net/10259/11235 10.1007/s13748-024-00331-2 2192-6360 Phytolith analysis is now an essential technique, both for the reconstruction of past environmental and climatic changes and for the study of anthropic and faunal plant use, in such disciplines as archaeology, paleoecology, paleonthology, and palynology. Currently, phytolith identification and categorisation involves time-consuming and tedious manual classification tasks that are not always error free. Automated phytolith classification will be key to the standardisation of phytolith identification processes, circumventing human error in the phytolith identification process. In this paper, a comparative analysis is presented of different types of feature sets, feature combinations, and classifier combinations (through stacking), and their use for automatic phytolith classification, including state-of-the-art vision transformers and convolutional neural networks, techniques which have shown remarkable progress within different areas, including computer vision. In this research, twenty-two different sets of features (three based on shape, sixteen on appearance, and three on texture) and six classifier strategies (single and combined via stacking) were compared. The experimental results revealed that texture-related features offered no valuable information for classification purposes. However, classification tasks were efficiently performed with strategies based on shape and appearance features (extracted using deep neural networks). More specifically, the use of those features combined with a stacking strategy, achieved better results than any other features and feature-based strategies, with an accuracy value of 98.32%. This work is supported by the Junta de Castilla y León under project BU055P20 (JCyL/FEDER, UE) and the Spanish Ministry of Science and Innovation under project PID2020-119894GB-I00 co-financed through European Union FEDER funds. application/pdf eng Springer Progress in Artificial Intelligence. 2024, V. 13, n. 3, p. 217-244 https://doi.org/10.1007/s13748-024-00331-2 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Phytolith classification Feature extraction Feature combination Stacking Aprendizaje automático Proceso de imágenes Machine learning Image processing Towards automatic phytolith classification using feature extraction and combination strategies info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Progress in Artificial Intelligence 13 3 217 244