A Matching Scheme Based on Proper Vector

To speed-up transfer rate, reduce communication bandwidth and storage space, image compression is needed. Owing to the feature of fractal compression, its retrieval for similar images is difficulty. A method is proposed to evaluate the similarity of images compressed. The method allows for comparing loss-compressed images and based upon the fractal framework of the iterated function systems (IFS) widely used for image compression. The image index is represented through a vector of numeric features, corresponding to contractive functions (CF) of the IFS framework. Because a compression code contains mapping information between similar regions in the same image, this mapping information can be treated as vectors. Also, it is important to generate the representative vectors using the mapping vectors. This representative vector can describes the features of the images, so the similarity between images is directly calculable from representative vectors. This similarity is applicable to image retrieval, and the scheme will be explained and demonstrated experimentally on its efficiency in this paper.