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 [1], 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 Nonlinear 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.

Kernelized Support Vector Machines (SVM) have gained the status of off-the-shelf classifiers, able to deliver state of the art performance on almost any problem. Still, their practical use is constrained by their computational and memory complexity, which grows super-linearly with the number of training samples. In order to retain the low training and testing complexity of linear classifiers and the flexibility of non linear ones, a growing, promising alternative is represented by methods that learn non-linear classifiers through local combinations of linear ones. In this paper we propose a new multi class local classifier, based on a latent SVM formulation. The proposed classifier makes use of a set of linear models that are linearly combined using sample and class specific weights. Thanks to the latent formulation, the combination coefficients are modeled as latent variables. We allow soft combinations and we provide a closed-form solution for their estimation, resulting in an efficient prediction rule. This novel formulation allows to learn in a principled way the sample specific weights and the linear classifiers, in a unique optimization problem, using a CCCP optimization procedure. Extensive experiments on ten standard UCI machine learning datasets, one large binary dataset, three character and digit recognition databases, and a visual place categorization dataset show the power of the proposed approach.

Indoor scenes are characterized by a high intra-class variability, mainly due to the intrinsic variety of the objects in them, and to the drastic image variations due to (even small) view-point changes. One of the main trends in the literature has been to employ representations coupling statistical characterizations of the image, with a description of their spatial distribution. This is usually done by combining multiple representations of different image regions, most often using a fixed 44, or pyramidal image-partitioning scheme. While these encodings are able to capture the spatial regularities of the problem, they are unsuitable to handle its spatial variabilities. In this work we propose to complement a traditional spatial-encoding scheme with a bottom-up approach designed to discover visual-structures regardless of their exact position in the scene. To this end we use saliency maps to segment each image in two regions: The most and least salient 50%. This segmentation provides a description of images which is somehow related to the relative semantics of the discovered regions, complementing the canonical spatial description. We evaluated the proposed technique on three public scene recognition datasets. Our results prove this approach to be effective in the indoor scenario, while being also meaningful for other scene categorization tasks.