Content-Based Image Retrieval using Fractal Orthonormal Basis

In this paper, a novel method is proposed to extract a stable feature set representative of image content. Each image is represented by a linear combination of fractal orthonormal basis vectors. The mapping coefficients of an image projected onto each orthonormal basis constitute a feature vector. The distance remains consistent, i.e., isometric embedded, between any image pairs before and after the projection onto orthonormal axes. Not only similar images generate points close to each other in the feature space, but also dissimilar ones produce feature points far apart. Therefore, utilizing coefficients derived from the proposed linear combination of fractal orthonormal basis as a key to search an image database will retrieve similar images; while at the same time exclude dissimilar ones. The coefficients associated with each image can be later used to reconstruct the original. The content-based query is performed in the compressed domain. This approach is efficient for content-based query.