Multiscale Context Features for Geological Image Classification

Matheus Todescato, Luan Garcia, Dennis Balreira, Joel Carbonera

2023

Abstract

Dealing with image retrieval in corporate systems becomes challenging when the dataset is small and the images present features in multiple scales. In this paper, we propose the notion of multiscale context features, in order to decrease information loss and improve the classification of images in such scenarios. We propose a preprocessing approach that splits the image into a set of patches, computes their features using a pre-trained model, and computes the context feature representing the whole image as an aggregation of the features extracted from individual patches. Besides that, we apply this approach in different scales of the image, generating context features of different scales, and we aggregate them to generate a multiscale representation of the image, which is used as the classifier input. We evaluated our method in a geological images dataset and in a publicly available dataset. We evaluate our approach with three efficient pre-trained models as feature extractors. The experiments show that our approach achieves better results than the conventional approaches for this task.

Paper Citation

in Harvard Style

Todescato M., Garcia L., Balreira D. and Carbonera J. (2023). Multiscale Context Features for Geological Image Classification. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-648-4, SciTePress, pages 407-418. DOI: 10.5220/0011981100003467

in Bibtex Style

@conference{iceis23,
author={Matheus Todescato and Luan Garcia and Dennis Balreira and Joel Carbonera},
title={Multiscale Context Features for Geological Image Classification},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2023},
pages={407-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011981100003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Multiscale Context Features for Geological Image Classification
SN - 978-989-758-648-4
AU - Todescato M.
AU - Garcia L.
AU - Balreira D.
AU - Carbonera J.
PY - 2023
SP - 407
EP - 418
DO - 10.5220/0011981100003467
PB - SciTePress