Name
Abstract | ||
|---|---|---|
A expansion encoding approach to image classification is presented. Localized principal components or “eigenpaxels” are used as a set of basis functions to represent images. That is, principal-component analysis is applied locally rather than on the entire image. The “eigenpaxels” are statistically determined using a database of the images of interest. Classification based on visual similarity is achieved through the use of a single-layer error-correcting neural network. Expansion encoding and the technique of subsampling are key elements in the processing stages of the eigenpaxel algorithm. Tested using a database of frontal face images consisting of 40 individuals, the algorithm exhibits equivalent performance to other comparable but more cumbersome methods. In addition, the technique is shown to be robust to various types of image noise |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/72.925566 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
eigenvalues and eigenfunctions,face recognition,image classification,image coding,neural nets,principal component analysis,PCA,basis functions,eigenpaxels,expansion encoding approach,frontal face image database,image classification,image noise,image representation,localized principal components,neural-network approach,principal-component analysis,single-layer error-correcting neural network,subsampling | Feature detection (computer vision),Computer science,Image processing,Artificial intelligence,Contextual image classification,Artificial neural network,Facial recognition system,Computer vision,Automatic image annotation,Pattern recognition,Image texture,Image noise,Machine learning | Journal |
Volume | Issue | ISSN |
12 | 3 | 1045-9227 |
Citations | PageRank | References |
9 | 0.80 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| McGuire, P. | 1 | 9 | 0.80 |
| D'Eleuterio, G.M.T. | 2 | 84 | 11.83 |