Name
Abstract | ||
|---|---|---|
This paper introduces a novel document-zone classification algorithm. Low level image features are first extracted from document zones and Partial Least Squares is used on pairs of classes to compute discriminating pairwise features. Rather than using the popular one-against-all and one-against-one voting schemes, we introduce a novel hybrid method which combines the benefits of the two schemes. The algorithm is applied on the University of Washington dataset and 97.3% classification accuracy is obtained. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4761553 | 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 |
Keywords | Field | DocType |
correlation,pixel,image classification,feature extraction,accuracy,image features,support vector machines,classification algorithms | Data mining,Pairwise comparison,Pattern recognition,Computer science,Feature (computer vision),Partial least squares regression,Support vector machine,Feature extraction,Pixel,Artificial intelligence,Contextual image classification,Statistical classification | Conference |
ISSN | Citations | PageRank |
1051-4651 | 5 | 0.48 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wael Abd-Almageed | 1 | 248 | 24.52 |
| Mudit Agrawal | 2 | 57 | 5.28 |
| Wontaek Seo | 3 | 10 | 1.47 |
| David Doermann | 4 | 4313 | 312.70 |