Hamming embedding similarity-based image classification
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Effective use of frequent itemset mining for image classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Learning class-to-image distance via large margin and l1-norm regularization
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Unsupervised and supervised visual codes with restricted boltzmann machines
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
A relational kernel-based framework for hierarchical image understanding
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
The pooled NBNN kernel: beyond image-to-class and image-to-image
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Naive bayes image classification: beyond nearest neighbors
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Locality discriminative coding for image classification
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Modeling local descriptors with multivariate gaussians for object and scene recognition
Proceedings of the 21st ACM international conference on Multimedia
JKernelMachines: a simple framework for kernel machine
The Journal of Machine Learning Research
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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.