Abstract
In this paper, a new method for generating size-bounded clusters is proposed such that the cardinality of each cluster is less than or equal to a pre-specified value. First, set estimation techniques coupled with Rectangular Intersection Graphs are used to generate adaptive clusters. Then, the size-bounded clusters are obtained by using space partitioning techniques. The clusters can be indexed by a Kd-tree like structure for similarity queries. The proposed method is likely to find applications to Content Based Image Retrieval (CBIR). © Springer-Verlag Berlin Heidelberg 2005.
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CITATION STYLE
Bhunre, P. K., Murthy, C. A., Bishnu, A., Bhattacharya, B. B., & Kundu, M. K. (2005). A hybrid data and space partitioning technique for similarity queries on bounded clusters. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3776 LNCS, pp. 544–550). https://doi.org/10.1007/11590316_86
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