Comparison of Wavelet Transforms and Fractal Coding in Texture-Based Image Retrieval

Image compression techniques based on wavelet and fractal coding have been recognized signiicantly useful in image texture classiication and discrimination. In fractal coding approach, each image is represented by a set of self-transformations through which an approximation of the original image can be reconstructed. These transformations of images can be utilized to distinguish images. The fractal coding technique can be extended to eeectively determine the similarity between images. We introduce a joint fractal coding technique, applicable to pairs of images, which can be used to determine the degree of their similarity. Our experimental results demonstrate that fractal code approach is eeective for content-based image retrieval. In wavelet transform approach, the wavelet transform decorrelates the image data into frequency domain. Feature vectors of images can be constructed from wavelet transformation, which can also be utilized to distinguish images through measuring distances between feature vectors. Our experiments indicate that this approach is also eeective on content-based similarity comparison between images. More speciically, we observe that wavelets transform approach performs more eeective on content-based similarity comparison on those images which contain strong texture features, where fractal coding approach performs relatively more uniformly well for various type of images.