Scene Recognition with Naive Bayes Non-linear Learning

A crucial feature of a good scene recognition algorithm is its ability to generalize. Scene categories, especially those related to human made indoor places or to human activities like sports, do present a high degree of intra-class variability, which in turn requires high robustness and generalization properties. Such features are amongst the distinctive characteristics of the Naive Bayes Nearest Neighbor (NBNN) approach, an image classification framework that since its introduction in 2008 has been gaining momentum in the visual recognition community. In this paper we show how with a straightforward modification of the original NBNN scoring function it is possible to use a recently introduced latent locally linear SVM algorithm to discriminatively learn a set of prototype local features for each class. The resulting classification algorithm, that we call Naive Bayes Non-linear Learning (NBNL) preserves the generality and robustness properties of the original approach, while greatly reducing its memory requirements during testing, and significantly improving its performance. To the best of our knowledge this is the first work to exploit the structure of the local features through the use of a latent locally linear discriminative learning method. Experiments over three different public scene recognition datasets show the effectiveness of the proposed algorithm, which outperforms several existing NBNN-based methods and is competitive with standard Bag-of-Words plus SVM approaches.