Digital image indexing and retrieval by content using the fractal transform for multimedia databases

Digital image database represent huge amount of data, automatic indexing and content base retrieval are crucial factors. Content base retrieval requests specific indexing techniques allowing to deal with efficient data representations (reduced amount of data and adapted to content base retrieval) in order to avoid prohibitive low level process time on queries. We present a method consisting in building an “iconic” level image representation presenting the semantic, allowing to process content base retrieval and to reconstruct the image. This representation consists in a set of function parameters. It is a semantic presenting compression built by using a dedicated fractal compress scheme. To search patterns in a huge set of images, a specific algorithm allows to use this “iconic” representation as an index. It works entirely in the fractal transform parameter space of both image and pattern, to obtain performances compatible with an interactive search. The research engine uses both textures and edges of the pattern. The pattern can be present in the image with different orientations and/or scales by using a multiresolution fractal representation of the pattern. This method allows to retrieve in 3 seconds a 64×64 pixels pattern in an 100 images (512×512 pixels) database, on a SUN Spare 20 workstation. It can be combined with other indexing and retrieval techniques, such as textual annotation