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 pr ocess, significant features must first be extracted from image data in their pixel format. These features must then be classified and indexe d to assist efficient access to image content. With the large volume o f visual data stored in a visual database, image classification is a c ritical step to achieve efficient indexing and retrieval. In this pape r, we investigate an effective approach to the clustering of image dat a based on the technique of fractal image coding, a method first intro duced in conjunction with fractal image compression technique. A joint fractal coding technique, applicable to pairs of images, is used to d etermine the degree of their similarity. Images in a visual database c an be categorized in clusters on the basis of their similarity to a se t of iconic images. Classification metrics are proposed for the measur ement of the extent of similarity among images. By experimenting on a large set of texture and natural images, we demonstrate the applicabil ity of these metrics and the proposed clustering technique to various visual database applications.