Apriori Data Mining on Rotationally Invariant Multiresolutional Moments for Pattern Recognition
Authors
Corresponding Author
Annupan Rodtook
Available Online October 2006.
- 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/).
Cite this article
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 -