Abstract
In this paper, we propose the effective similarity measures on which the similarity-based pattern retrieval is based. Both data sequences and query sequences are partitioned into segments, and the query processing is based upon the comparison of the features between data and query segments, instead of scanning all data elements of entire sequences. We conduct experiments on multidimensional data sequences that are generated by extracting features from video streams, and show the effectiveness of the proposed measures.
This work was supported by the Korea Research Foundation Grant funded by Korean Government (MOEHRD) (R05-2004-000-10972-0).
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© 2005 Springer-Verlag Berlin Heidelberg
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Lee, SL., Lee, JH., Chun, SJ. (2005). On the Effective Similarity Measures for the Similarity-Based Pattern Retrieval in Multidimensional Sequence Databases. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_94
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DOI: https://doi.org/10.1007/11540007_94
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