Our thesis is that a Bayesian sensor fusion framework for high-level vision has quantifiable advantages over other popular methods such as Neural Nets. Bayesian networks allow for the development of a probabilistic sensor fusion scheme for combining evidences from multiple information sources. The combined information can then be used to solve a variety of problems across multiple domains. Evidence regarding the label for a region can come from various sources. In order to perform effective sensor fusion, we need to accept this multimodal evidence in a probabilistic framework.
Bayesian networks provide a suitable framework for such evidence combination.
We present details of knowledge engineering techniques designed to create the Bayesian network structures for sensor fusion, as well as the training methodologies. The theoretical framework is then applied to solve various region labeling problems. Automatic main subject detection provides a measure of saliency for regions or objects in an image. A large number of feature detectors, some based on the physics of the camera, and others based on computer vision and image understanding techniques, can provide evidence for main subject region in a consumer photographic image. The Bayesian sensor fusion framework allows us to construct a network that quickly combines this evidence to produce a belief map that identifies main subject regions in the image.