The NBNN kernel

Naive Bayes Nearest Neighbor (NBNN) has recently been proposed as a powerful, non-parametric approach for object classification, that manages to achieve remarkably good results thanks to the avoidance of a vector quantization step and the use of image-to-class comparisons, yielding good generalizati...

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Bibliographic Details
Main Authors: Tuytelaars, T., Fritz, M., Saenko, K., Darrell, T.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Algorithm design and analysis
Feature extraction
Kernel
Support vector machines
Training
Vectors
Online Access:Request full text
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Summary:Naive Bayes Nearest Neighbor (NBNN) has recently been proposed as a powerful, non-parametric approach for object classification, that manages to achieve remarkably good results thanks to the avoidance of a vector quantization step and the use of image-to-class comparisons, yielding good generalization. In this paper, we introduce a kernelized version of NBNN. This way, we can learn the classifier in a discriminative setting. Moreover, it then becomes straightforward to combine it with other kernels. In particular, we show that our NBNN kernel is complementary to standard bag-of-features based kernels, focussing on local generalization as opposed to global image composition. By combining them, we achieve state-of-the-art results on Caltech101 and 15 Scenes datasets. As a side contribution, we also investigate how to speed up the NBNN computations.
ISSN:1550-5499
2380-7504
DOI:10.1109/ICCV.2011.6126449