Name
Abstract | ||
|---|---|---|
The bags of feature and feature vocabulary based approaches have been presented for image categorization due to their simplicity and competitive performance. Some modified versions have been subsequently proposed, incorporating the methods such as adapted vocabularies, fast indexing, and Gaussian mixture models. In this paper, we propose an improvement of replacing the Harris-affine detection method by a random sampling procedure together with an increased number of sample points. Experimental results show that this new method improves categorization accuracy on a five-category problem using the Caltech-4 dataset. It is concluded that random sampling produces higher attainable point density and better categorization performance. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1142/S0218001411008828 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | DocType | Volume |
Image categorization, feature vocabulary, feature detection, support vector machine | Journal | 25 |
Issue | ISSN | Citations |
3 | 0218-0014 | 0 |
PageRank | References | Authors |
0.34 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Y. Shih | 1 | 1103 | 89.56 |
| Alexander Sheppard | 2 | 0 | 0.34 |