While most image classification methods to date are based on image-to-image comparisons, Boiman
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.