The NBNN Kernel
Title | The NBNN Kernel |
Publication Type | Conference Paper |
Year of Publication | 2011 |
Authors | Tuytelaars, T., Fritz M., Saenko K., & Darrell T. |
Page(s) | 1824-1831 |
Other Numbers | 3231 |
Abstract | Naive Bayes Nearest Neighbor (NBNN) has recentlybeen proposed as a powerful, non-parametric approach forobject classification, that manages to achieve remarkablygood results thanks to the avoidance of a vector quantizationstep and the use of image-to-class comparisons, yieldinggood generalization. In this paper, we introduce a kernelizedversion of NBNN. This way, we can learn the classifierin a discriminative setting. Moreover, it then becomesstraightforward to combine it with other kernels. In particular,we show that our NBNN kernel is complementary tostandard bag-of-features based kernels, focussing on localgeneralization as opposed to global image composition. Bycombining them, we achieve state-of-the-art results on Caltech101and 15 Scenes datasets. As a side contribution, wealso investigate how to speed up the NBNN computations. |
URL | http://www.icsi.berkeley.edu/pubs/vision/NBNNkernel11.pdf |
Bibliographic Notes | Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain, pp. 1824-1831 |
Abbreviated Authors | T. Tuytelaars, M. Fritz, K. Saenko, and T. Darrell |
ICSI Research Group | Vision |
ICSI Publication Type | Article in conference proceedings |