Image indexing and similarity retrieval based on spatial relationship model
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Introduction to multimedia and mobile agents
Multidimensional data structures for spatial applications
Algorithms and theory of computation handbook
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Spatial access methods(SAMs) are often used as clustering indexes in spatial database systems. Therefore, a SAM should have the clustering property both in the index and in the data file.In this paper, we argue that corner transformation preserves the clustering property such that objects having similar sizes and positions in the original space tend to be placed in the same region in the transform space. We then show that SAMs based on corner transformation are able to maintain clustering both in the index and in the data file for storage systems with fixed object positions and propose the MBR-MLGF as an example to implement such an index.Extensive experiments comparing with the R$^*$-tree show that corner transformation indeed preserves the clustering property. This result reverses the common belief that transformation will adversely affect the clustering.