Global Gaussian Approach for Scene Categorization Using Information Geometry

Abstract

Local features provide powerful cues for generic image recognition. An image is represented by a "bag" of local features, which form a probabilistic distribution in the feature space. The problem is how to exploit the distributions efficiently. One of the most successful approaches is the bag-of-keypoints scheme, which can be interpreted as sparse sampling of high-level statistics, in the sense that it describes a complex structure of a local feature distribution using a relatively small number of parameters. In this paper, we propose the opposite approach, dense sampling of low-level statistics. A distribution is represented by a Gaussian in the entire feature space. We define some similarity measures of the distributions based on an information geometry framework and show how this conceptually simple approach can provide a satisfactory performance, comparable to the bag-of-keypoints for scene classification tasks. Furthermore, because our method and bag-of-keypoints illustrate different statistical points, we can further improve classification performance by using both of them in kernels.