Grassmann matching kernels for scene representation and recognition

In this paper we propose a new method for scene representation and recognition based on the concept of Region Subspaces. Each image is pre-segmented into semantically meaningful regions and local features are extracted at different scales from each such region. The Region Subspaces are the low-dimensional linear subspaces calculated from the set of local features inside each region. We also define Grassmann Matching Kernels (GMK), which extend Grassmann Kernels to be able to match simultaneously multiple subspaces. We call the resulting method, which represents image scenes through a set of Region Subspaces and matches them across different categories through the Grassmann Matching Kernel, Subspaces-of-Regions (SoR) and illustrate it on the 15-Scene dataset.