Interpretation of natural scenes using multi-parameter default models and qualitative constraints

High variability of object features and bad local feature separation of object classes are the main causes for the difficulties encountered during the interpretation of ground-level natural scenes. The authors propose a knowledge-based scene classification and segmentation method. The first stage carries out object classification on the basis of known local feature default models. It then searches for regions which satisfy qualitative constraints in the classified object images and selects good, approximate object region candidates, using multiple features. This approach is constrained in a top-down manner by the local feature default model and qualitative constraints while at the same time operating in a data-driven style through automatic threshold value and window size selection mechanisms. The second stage extends and refines the result of the first stage by performing a constrained search for object boundaries