We discuss how an orthogonalized fractal decoding algorithm can be used to derive a fractal decomposition of the encoded image into a dc and a set of ac components that characterize the edge information in the image. The dc component can be characterized by its histogram. The ac components provide a model for the texture, and the distribution of each ac component can be well represented by a generalized Gaussian density (GGD), which can be efficiently characterized by two GGD parameters. Such characterization of the accomponents is exploited in this work for texture image retrieval, and experimental results show that our proposed indexing technique based on our novel fractal dc and ac signature provides superior retrieval performance compared to current fractal indexing techniques. Moreover, the performance of our approach is comparable to the state of the art wavelet-based method at a fraction of the retrieval time.
Scopus Subject Areas
- Atomic and Molecular Physics, and Optics
- Computer Science Applications
- Electrical and Electronic Engineering