CLASS ASSOCIATION RULES WITH OCCURRENCE COUNT IN IMAGE CLASSIFICATION

Abstract

The concept of utilizing association rules for classification has emerged in recent years. This approach has often proved to be more efficient and accurate than traditional techniques. In this paper we extend the existing associative classifier building algorithms and apply them to the problem of image classification. We describe a set of photographs with features calculated on the basis of their color and texture characteristics and experiment with different types of rules which use the information about the existence of a particular feature in an image, its occurrence count and spatial proximity to classify the images accurately. We suggest using association rules more closely tied to the nature of the image data and compare the results with those of classification with simpler rules, taking into consideration only the existence of a particular feature on an image.