Relation Between the Object Recognition Rate and the Matching Accuracy of Local Descriptors
| Language | Japanese |
|---|---|
| Type | Proceedings of Japanese Conferences |
| Authors | Kazuto NOGUCHI, Koichi KISE, Masakazu IWAMURA |
| Title | Relation Between the Object Recognition Rate and the Matching Accuracy of Local Descriptors |
| Journal | Proceedings of MIRU 2008 |
| Presentation number | IS4-4 |
| Pages | p.504 |
| Reviewed | Not reviewed |
| Date | July 2008 |
| Research fields | Pattern recognition, Object Recognition |
| Key | Noguchi2008_MIRU_Relation (id=1025) |
Abstract
A simple method of object recognition with local descriptors is voting by matching local descriptors with nearest neighbor (NN) search. Because the number of local descriptors is so large, a key technology for this approach is to reduce the computational cost of matching. It is known that the cost of NN search can be drastically cut by approximation, though it poses another question of how accurate the matching of local descriptors should be for accurate object recognition. The major contribution of this report is twofold. First, we experimentally show that it is not necessary for matching to be accurate. For example, an object recognition rate of 97 % with 100,000 object models is achieved by the matching accuracy of only 14%. Secondly, we propose a model of recognition by voting based on a simple binomial distribution, which agrees with experimental results.
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