Efficient Classification of Digital Images based on Pattern-features

Angelo Furfaro, Simona E. Rombo

2018

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.

Paper Citation

in Harvard Style

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 - Volume 1: PhyCS, ISBN 978-989-758-329-2, pages 93-99. DOI: 10.5220/0006955500930099

in Bibtex Style

@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 - Volume 1: PhyCS,},
year={2018},
pages={93-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006955500930099},
isbn={978-989-758-329-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Efficient Classification of Digital Images based on Pattern-features
SN - 978-989-758-329-2
AU - Furfaro A.
AU - Rombo S.
PY - 2018
SP - 93
EP - 99
DO - 10.5220/0006955500930099