2026-04-27T18:35:00Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/112352026-01-17T01:05:29Zcom_10259_5377com_10259_5086com_10259_2604col_10259_5378
Repositorio Institucional de la Universidad de Burgos
author
Diez Pastor, José Francisco
author
Latorre Carmona, Pedro
author
Arnaiz González, Álvar
author
Canepa Oneto, Antonio Jesús
author
Ruiz Pérez, Javier
author
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%.
eng
Atribución 4.0 Internacional
Phytolith classification
Feature extraction
Feature combination
Stacking
Towards automatic phytolith classification using feature extraction and combination strategies
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/11235/1/D%c3%adez-piai_2024.pdf
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