CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Mercer Kernels for Object Recognition with Local Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Tree-Structured Support Vector Machines for Multi-class Classification
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Constructing Category Hierarchies for Visual Recognition
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
What does classifying more than 10,000 image categories tell us?
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Beyond spatial pyramids: Receptive field learning for pooled image features
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Discriminative feature fusion for image classification
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Ask the locals: Multi-way local pooling for image recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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While most image classification methods to date are based on image-to-image comparisons, Boiman et al. have shown that better generalization can be obtained by performing image-to-class comparisons. Here, we show that these are just two special cases of a more general formulation, where the feature space is partitioned into subsets of different granularity. This way, a series of representations can be derived that trade-off generalization against specificity. Thereby we show a connection between NBNN classification and different pooling strategies, where, in contrast to traditional pooling schemes that perform spatial pooling of the features, pooling is performed in feature space. Moreover, rather than picking a single partitioning, we propose to combine them in a multi kernel framework. We refer to our method as the Pooled NBNN kernel. This new scheme leads to significant improvement over the standard image-to-image and image-to-class baselines, with only a small increase in computational cost.