Abstract
Semantic scene classification is still a challenging problem in computer vision. In contrast to the common approach of using low-level features computed from the scene, our approach uses explicit semantic object detectors and scene configuration models. To overcome faulty semantic detectors, it is critical to develop a region-based, generative model of outdoor scenes based on characteristic objects in the scene and spatial relationships between them. Since a fully connected scene configuration model is intractable, we chose to model pairwise relationships between regions and estimate scene probabilities using loopy belief propagation on a factor graph. We demonstrate the promise of this approach on a set of over 2000 outdoor photographs, comparing it with existing discriminative approaches and those using low-level features.
