Recursive Tower of Knowledge for Learning to Interpret Scenes

The Tower of Knowledge architecture integrates probability theory and logic for making decisions. The scheme models the causal dependencies between the functionalities of objects and their descriptions, and then employs the maximum expected utility principle, which combines probability theory and logic, to select the most appropriate label for the object. Since most existing scene interpretation methods rely heavily on training data, we develop in this paper a recursive version of ToK to avoid such dependency. Recursive ToK learns the prior distributions iteratively from the decisions of labelling components made in the last iteration, partly by functionalities of components, and partly by the already learnt prior distributions in previous iterations. To validate our method in the domain of 3D outdoor scene interpretation, we compare ToK against a state-of-the-art method, Expandable Bayesian Networks (EBN), for labelling components of buildings. Experimental results then show that the labelling accuracy of ToK is superior to that of EBN. Also, these results reveal that recursive ToK improves the accuracy of ToK for labelling 3D components in the worst case when lacking any training data.