Name
Abstract | ||
|---|---|---|
This paper presents a new visual feature representation method called the weighted census transform (WCT) based on modified census transform (MCT) and entropy information of training dataset. The proposed feature representation model can offer robustness to represent the same visual images such as MCT feature and sensitivity to effectively classify different visual images. In order to enhance the sensitivity of MCT feature, we designed the different weights for each MCT feature as binary code bit by statistical approach with the training dataset. In order to compare the proposed feature with MCT feature, we fixed classification method such as compressive sensing technique for two features. Experimental results shows that proposed WCT features have better classification performance than traditional MCT features for AR face datasets. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/URAI.2013.6677409 | URAI |
Keywords | Field | DocType |
weighted census transform,visual feature representation method,image representation,binary code bit,face recognition,ar face datasets,modified census transform,wct,statistical analysis,feature representation model,visual image classification,feature representation,training dataset,compressed sensing,image classification,statistical approach,mct,entropy information,transforms,entropy,compressive sensing technique,pattern classification | Facial recognition system,Computer vision,Pattern recognition,Feature (computer vision),Computer science,Binary code,Robustness (computer science),Census transform,Artificial intelligence,Contextual image classification,Entropy (information theory),Compressed sensing | Conference |
ISSN | ISBN | Citations |
2325-033X | 978-1-4799-1195-0 | 1 |
PageRank | References | Authors |
0.36 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sungmoon Jeong | 1 | 99 | 15.05 |
| Hosun Lee | 2 | 10 | 4.66 |
| Younes El Hamdi | 3 | 1 | 0.36 |
| Nak Young Chong | 4 | 403 | 56.29 |