Abstract
This paper first presents a new oRGB-SIFT descriptor, and then integrates it with other colour SIFT features to produce the novel Colour SIFT Fusion (CSF) and the Colour Greyscale SIFT Fusion (CGSF) descriptors for image classification with special applications to biometrics. Classification is implemented using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Nearest Neighbour (KNN) decision rule. The effectiveness of the proposed descriptors and classification method are evaluated using 20 image categories from two large scale, grand challenge datasets: the Caltech 256 database and the UPOL Iris database.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 56-75 |
| Number of pages | 20 |
| Journal | International Journal of Biometrics |
| Volume | 3 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2011 |
| Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics
Keywords
- Biometrics
- CGSF
- CSF
- Colour SIFT fusion
- Colour greyscale SIFT fusion
- EFM-KNN classifier
- Image classification
- ORGB-SIFT descriptor