Image classification: Are rule-based systems effective when classes are fixed and known?

In this paper, we investigate if rule-based systems are useful for image classification problems when the number of classes is fixed. The rules are derived from simple edge features such as width and straightness. A class representative is calculated for each class according to the average percentage of edges that satisfy the rule for a particular class. This percentage for an unknown image is compared to the class representative to assign a label to it. The proposed system does not require extensive feature extraction and classification techniques. It is shown that the rule based system outperforms some of the reported results on scene classification.