Towards automatic phytolith classification using feature extraction and combination strategies
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 categ...
| Autores: | , , , , , |
|---|---|
| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/370222 |
| Acesso em linha: | http://hdl.handle.net/10261/370222 |
| Access Level: | acceso abierto |
| Palavra-chave: | Phytolith classification Feature extraction Feature combination Stacking |
| Resumo: | 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%. |
|---|