Name
Abstract | ||
|---|---|---|
In this paper, two novel image feature extraction algorithms based on directional filter banks and nearest feature line are proposed, which are named Single Directional Feature Line Discriminant Analysis (SD-NFDA) and Multiple Directional Feature Discriminant Line Analysis (MD-NFDA). SD-NFDA and MD-NFDA extract not only the statistic feature of samples, but also the directionality feature. SD-NFDA and MD-NFDA can get higher average recognition rate with less running time than other nearest feature line based feature extraction algorithms. Experimental results confirm the advantages of SD-NFDA and MD-NFDA. |
Year | Venue | Keywords |
|---|---|---|
2012 | INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2012), PT II | Directional Filter Bank,Nearest Feature Line,Feature Extraction |
Field | DocType | Volume |
Dimensionality reduction,Feature extraction algorithm,Computer science,Feature (machine learning),Artificial intelligence,k-nearest neighbors algorithm,Statistic,Pattern recognition,Discriminant,Feature extraction,Speech recognition,Linear discriminant analysis,Machine learning | Conference | 7197 |
ISSN | ISBN | Citations |
0302-9743 | 978-3-642-28490-8 | 0 |
PageRank | References | Authors |
0.34 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Lijun | 1 | 37 | 9.40 |
| Chu Shu-Chuan | 2 | 425 | 53.51 |
| John F. Roddick | 3 | 1908 | 331.20 |
| Pan Jeng-Shyang | 4 | 2466 | 269.74 |