Object-oriented representation of image space by puzzletrees

The objective of this paper is to propose a syntactic formalism for space representation. Beside the well known advantages of hierarchical data structure, this underlying approach has the additional strength of self-adapting to a spatial structure at hand. The approach is called puzzletree because its recursive decomposition of an image results in a number of rectangular regions which in a certain order--like a puzzle--reconstruct the original image. The approach may also be applied to higher-dimensioned spaces. This paper concentrates on the principles of puzzletrees by explaining the underlying heuristic for their generation and outlining their use to facilitate higher-level operations like image segmentation or object recognition. Finally, results are shown and a comparison to conventional region quadtrees is done.