EFFICIENT IMAGE REDUCTION FOR FAST INTELLIGIBLE CLASSIFICATION

Marc Joliveau

2010

Abstract

In the past decades, many domains collected great amounts of data, particularly multimedia files, and stored them in large databases. Therefore, area such as similarity search for image learning have received much attention in the recent years. This paper presents an innovative way to strongly reduce dimension and keep relations between components of an image data set. Our method is validated on the Mnist learning database containing 70000 pictures of handwritten digits. Results demonstrate that the proposed approach is very efficient. It allows to accurately classify, learn, and identify digits using very short computation time in comparison with those obtained with original full-size images.

Paper Citation

in Harvard Style

Joliveau M. (2010). EFFICIENT IMAGE REDUCTION FOR FAST INTELLIGIBLE CLASSIFICATION . In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 157-162. DOI: 10.5220/0002691801570162

in Bibtex Style

@conference{icaart10,
author={Marc Joliveau},
title={EFFICIENT IMAGE REDUCTION FOR FAST INTELLIGIBLE CLASSIFICATION},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={157-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002691801570162},
isbn={978-989-674-021-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - EFFICIENT IMAGE REDUCTION FOR FAST INTELLIGIBLE CLASSIFICATION
SN - 978-989-674-021-4
AU - Joliveau M.
PY - 2010
SP - 157
EP - 162
DO - 10.5220/0002691801570162