Indoor vs outdoor classification of consumer photographs using low-level and semantic features

Scene categorization to indoor vs outdoor may be approached by using low-level features for inferring high-level information about the image. Low-level features such as color and texture have been used extensively in image understanding research, however, they cannot solve the problem completely. In this paper, we propose the use of a Bayesian network for integrating knowledge from low-level and semantic features for indoor vs outdoor classification of images. Using ground truth data for sky and grass detection, we demonstrate that the classification performance can be significantly improved when semantic features are employed in the classification process.