Name
Abstract | ||
|---|---|---|
The differences in performance of a range of interest operators are examined in a null hypothesis framework using McNemar's test on a widely-used database of images, to ascertain whether these apparent differences are statistically significant. It is found that some performance differences are indeed statistically significant, though most of them are at a fairly low level of confidence, i.e. with about a 1-in-20 chance that the results could be due to features of the evaluation database. A new evaluation measure i.e. accurate homography estimation is used to characterize the performance of feature extraction algorithms.Results suggest that operators employing longer descriptors are more reliable. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23678-5_51 | CAIP (2) |
Keywords | Field | DocType |
longer descriptors,widely-used database,interest operator,low level,new evaluation measure,feature extraction algorithm,accurate homography estimation,performance difference,apparent difference,evaluation database,feature extraction,mcnemar s test,homography | Computer vision,McNemar's test,Pattern recognition,Computer science,Null hypothesis,Feature extraction,Homography,Artificial intelligence,Operator (computer programming),Statistics,Confidence interval | Conference |
Volume | ISSN | Citations |
6855 | 0302-9743 | 2 |
PageRank | References | Authors |
0.46 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nadia Kanwal | 1 | 59 | 7.00 |
| Shoaib Ehsan | 2 | 110 | 24.43 |
| Adrian F. Clark | 3 | 221 | 72.99 |