Apriori Data Mining on Rotationally Invariant Multiresolutional Moments for Pattern Recognition

DOI

10.2991/jcis.2006.260How to use a DOI?

Keywords

filter bank, the Kullback-Leibler distance, Apriori mining algorithm, fuzzy C-mean clustering

Abstract

We propose a new feature selection procedure based on a combination of a pruning algorithm, Apriori mining techniques and fuzzy C-mean clustering. The feature selection algorithm is designed to mine on a multiresolution filter bank composed of rotationally invariant moments. The numerical experiments, with more than 10,000 images, demonstrate an accuracy increase of about 5% for a low noise, 15% for an average noise and 20% for a high-level noise.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Annupan Rodtook
AU  - Stanislav Makhanov
PY  - 2006/10
DA  - 2006/10
TI  - Apriori Data Mining on Rotationally Invariant Multiresolutional Moments for Pattern Recognition
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.260
DO  - 10.2991/jcis.2006.260
ID  - Rodtook2006/10
ER  -