Detail of Publication
| Text Language | Japanese |
|---|---|
| Authors | Kazuto Noguchi, Tomohiro Nakai, Koichi Kise, and Masakazu Iwamura |
| Title | Experimental Investigation of Relation Between Near Neighbor Search Methods for Feature Vectors and Efficiency of Object Recognition |
| Journal | IEICE Technical Report |
| Vol. | 106 |
| No. | PRMU-229 |
| Presentation number | PRMU2006-68 |
| Pages | pp.57-64 |
| Reviewed or not | Not reviewed |
| Month & Year | September 2006 |
| Abstract | Efficiency of object recognition methods using local descriptors such as SIFT and PCA-SIFT depends largely on the speed of matching between feature vectors since images are described by a large number of feature vectors. Because the matching is considered to be ``nearest neighbor (NN) search'' of feature vectors, the problem is paraphrased by ``how to make the NN search efficient''. For the object recognition, it is required that the number of incorrect matching does not exceed that of correct matching. In other words, a certain number of incorrect matching is acceptable. This observation allows us to make NN search more efficient using approximate NN search with reduced distance calculation. For this purpose, we propose two methods: one is to eliminate feature vectors that require a number of distance calculations. The other is to use no distance calculation. From experimental results with 10,000 database images and 2,000 query images, it is shown that the proposed method is two to three times efficient as compared to a method using ANN and can achieve, recognition rate of 98% with 8.3 ms/query. |
- Note
CVIM155-9 - Following file is available.
- Entry for BibTeX
@InCollection{Noguchi2006, author = {Kazuto Noguchi and Tomohiro Nakai and Koichi Kise and Masakazu Iwamura}, title = {Experimental Investigation of Relation Between Near Neighbor Search Methods for Feature Vectors and Efficiency of Object Recognition}, booktitle = {IEICE Technical Report}, year = 2006, month = sep, volume = {106}, number = {PRMU-229}, presenID = {PRMU2006-68}, pages = {57--64} }