Summary
Fractal based image coding has been shown to work well. The main reason is the ability to capture much significant information while discarding most of the redundancy. Therefore, a similar theoretical apparatus can be used to design a system that extracts information suitable for content based image indexing. After introducing the basics of partitioned iterated function systems as used in image processing, the structure of a fractal based image indexing system is described by showing how it evolved and developed over time, going from the image coding-compression stage through a histogram based approach (first and fire) to a more sophisticated and complex system (fine) that includes Peano-serialized spatial addressing, a linearized image space, a custom clustering strategy, ad-hoc search improving heuristics and specially defined distance functions. The resulting system is invariant or robust to a large class of typical variations that appear in natural images including rotations, scaling, and changes in color or illumination. The performance of fine is illustrated, discussed and compared with other contemporary alternatives using standard and custom-based image databases, mostly of single objects lying against a uniform background. Finally, some possible future developments are proposed with the ultimate goal of being able to deal with more complex pictorial scenes.
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De Marsico, M., Distasi, R., Nappi, M., Riccio, D. (2009). Fractal Based Image Indexing and Retrieval. In: Kocarev, L., Galias, Z., Lian, S. (eds) Intelligent Computing Based on Chaos. Studies in Computational Intelligence, vol 184. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-95972-4_4
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