Name
Abstract | ||
|---|---|---|
The theory of compressive sampling involves making random linear projections of a signal. Provided signal is sparse in some basis, small number of such measurements preserves the information in the signal, with high probability. Following the success in signal reconstruction, compressive framework has recently proved useful in classification, particularly hypothesis testing. In this paper, conventional random projection scheme is first extended to the image domain and the key notion of concentration of measure is closely studied. Findings are then employed to develop a 2D compressive classifier (2D-CC) for sparse images. Finally, theoretical results are validated within a realistic experimental framework. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICIP.2009.5414298 | Image Processing |
Keywords | Field | DocType |
eye,image coding,random processes,2d compressive classifier,compressive sampling theory,compressive sampling,random projection scheme,sparse images,retinal identification,image reconstruction,two dimensional compressive classifier,image sampling,random linear projections,image classification,signal reconstruction,conventional random projection scheme,random projections,data mining,concentration of measure,sparse matrices,feature extraction,hypothesis test,pixel,noise measurement | Random projection,Computer vision,Pattern recognition,Computer science,Stochastic process,Artificial intelligence,Classifier (linguistics),Contextual image classification,Sparse matrix,Compressed sensing,Signal reconstruction,Statistical hypothesis testing | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-5655-0 | 978-1-4244-5655-0 | 1 |
PageRank | References | Authors |
0.35 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Armin Eftekhari | 1 | 129 | 12.42 |
| Hamid Abrishami Moghaddam | 2 | 219 | 22.96 |
| Massoud Babaie-Zadeh | 3 | 912 | 66.33 |
| M. Shahram Moin | 4 | 15 | 2.88 |