A fractal-based clustering approach in large visual database systems

Large visual database systems require effective and efficient ways of indexing and accessing visual data on the basis of content. In this process, significant features must first be extracted from image data in their pixel format. These features must then be classified and indexed to assist efficient access to image content. With the large volume of visual data stored in a visual database, image classification is a critical step to achieve efficient indexing and retrieval. In this paper, we investigate an effective approach to the clustering of image data based on the technique of fractal image coding, a method first introduced in conjunction with fractal image compression technique. A joint fractal coding technique, applicable to pairs of images, is used to determine the degree of their similarity. Images in a visual database can be categorized in clusters on the basis of their similarity to a set of iconic images. Classification metrics are proposed for the measurement of the extent of similarity among images. By experimenting on a large set of texture and natural images, we demonstrate the applicability of these metrics and the proposed clustering technique to various visual database applications.