Image interpretation with fuzzy-graph based genetic algorithm

Image interpretation is a very challenging problem in the scope of computer vision. A novel method based on fuzzy graph and genetic algorithm is presented in this paper. It consists of two parts. At first, the fuzzy classification memberships are obtained by fuzzy classifier based on the statistic/geometric unary features of segmented regions, and a fuzzy graph used for describing interpretation information is built through the spatial binary features and the prior rule-base concerning spatial relations that is acquired from statistics or experience. Second, genetic searching algorithm is used to combine unary and binary features, realize uncertain analysis on the graph with loop, and achieve an optimistic interpretation result. Moreover this method can deal with the case where the image segmentation result is not precise. The simulation results are promising for this novel image interpretation method. It is an improvement over the one-directional reasoning method based image interpretation such as probabilistic, evidence and fuzzy reasoning