Image interpretation is an important part of computer vision

which is related to many fields such as image processing

classifier designing and logic reasoning. In this paper

genetic searching based two directional reasoning is discussed. The algorithm consists of two steps. At first

the fuzzy memberships of classification is obtained by fuzzy classifier based on the statistic/geometric features of segmented regions

and a fuzzy graph used for effectively representing interpretation information is constructed through prior rule base concerning about spatial relations that is acquired from statistics or experience. At second

genetic searching algorithm is used to combine the above two types of information

and the optimistic interpretation is achieved. In order to decrease the computational cost and increase the possibility of getting optimal solution

a new crossover operator of genetic searching is proposed that is based on non random graph partition. The experiments show that genetic searching based fuzzy image interpretation is powerful for the regions that include one or more objects. This method is an improvement over the one directional reasoning method based image interpretation such as probabilistic

evidence and fuzzy reasonings.