Name
Abstract | ||
|---|---|---|
Many computer vision algorithms require searching a set of images for similar patches, which is a very expensive operation. In this work, we compare and evaluate a number of nearest neighbors algorithms for speeding up this task. Since image patches follow very different distributions from the uniform and Gaussian distributions that are typically used to evaluate nearest neighbors methods, we determine the method with the best performance via extensive experimentation on real images. Furthermore, we take advantage of the inherent structure and properties of images to achieve highly efficient implementations of these algorithms. Our results indicate that vantage point trees, which are not well known in the vision community, generally offer the best performance. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-88688-4_27 | ECCV (2) |
Keywords | Field | DocType |
nearest neighbors method,similar patches,good nearest neighbors algorithm,computer vision algorithm,efficient implementation,nearest neighbors algorithm,different distribution,gaussian distribution,best performance,vision community,extensive experimentation,expensive operation,nearest neighbor,computer vision,nearest neighbor method | Computer science,Best bin first,Implementation,Computer vision algorithms,Nearest-neighbor chain algorithm,Artificial intelligence,Nearest neighbor search,Computer vision,Pattern recognition,Algorithm,Gaussian,Real image,Machine learning | Conference |
Volume | ISSN | Citations |
5303 | 0302-9743 | 58 |
PageRank | References | Authors |
3.18 | 16 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neeraj Kumar | 1 | 1623 | 74.67 |
| Li Zhang | 2 | 2286 | 151.94 |
| Shree K. Nayar | 3 | 12394 | 1538.46 |