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Authors: Angelo Furfaro 1 and Simona E. Rombo 2

Affiliations: 1 DIMES, University of Calabria and Italy ; 2 DMI, University of Palermo and Italy

Keyword(s): Image Classification, Bidimesional Pattern Extraction, Irredundant Pattern.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: Selecting a suitable set of features, which is able to represent the data to be processed while retaining the relevant distinctive information, is one of the most important issues in classification problems. While different features can be extracted from the raw data, only few of them are actually relevant and effective for the classification process. Since relevant features are often unknown a priori, many candidate features are usually introduced. This degrades both the speed and the predictive accuracy of the classifier due to the presence of redundancy in the feature candidate set. We propose a class of features for image classification based on the notion of irredundant bidimensional pair-patterns, and we present an algorithm for image classification based on their extraction. The devised technique scales well on parallel multi-core architectures, as witnessed by the experimental results that have been obtained exploiting a benchmark image dataset.

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Paper citation in several formats:
Furfaro, A. and Rombo, S. (2018). Efficient Classification of Digital Images based on Pattern-features. In Proceedings of the 5th International Conference on Physiological Computing Systems - PhyCS, ISBN 978-989-758-329-2; ISSN 2184-321X, pages 93-99. DOI: 10.5220/0006955500930099

@conference{phycs18,
author={Angelo Furfaro. and Simona E. Rombo.},
title={Efficient Classification of Digital Images based on Pattern-features},
booktitle={Proceedings of the 5th International Conference on Physiological Computing Systems - PhyCS,},
year={2018},
pages={93-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006955500930099},
isbn={978-989-758-329-2},
issn={2184-321X},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Physiological Computing Systems - PhyCS,
TI - Efficient Classification of Digital Images based on Pattern-features
SN - 978-989-758-329-2
IS - 2184-321X
AU - Furfaro, A.
AU - Rombo, S.
PY - 2018
SP - 93
EP - 99
DO - 10.5220/0006955500930099

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