RT info:eu-repo/semantics/article
T1 Towards automatic phytolith classification using feature extraction and combination strategies
A1 Diez Pastor, José Francisco
A1 Latorre Carmona, Pedro
A1 Arnaiz González, Álvar
A1 Canepa Oneto, Antonio Jesús
A1 Ruiz Pérez, Javier
A1 Zurro, Débora
K1 Phytolith classification
K1 Feature extraction
K1 Feature combination
K1 Stacking
K1 Aprendizaje automático
K1 Machine learning
K1 Proceso de imágenes
K1 Image processing
AB 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%.
PB Springer
SN 2192-6352
YR 2024
FD 2024-07
LK https://hdl.handle.net/10259/11235
UL https://hdl.handle.net/10259/11235
LA eng
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 27-abr-2026