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).
This work was supported in part by a grant from Intel Corp., USA (PO #CAC042717000).
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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: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_86
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DOI: https://doi.org/10.1007/11590316_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30506-4
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