Similarity Search Algorithm Based on Hyper-Rectangular Representation of Video Data Sets


The KIPS Transactions:PartD, Vol. 11, No. 4, pp. 823-834, Aug. 2004
10.3745/KIPSTD.2004.11.4.823,   PDF Download:

Abstract

In this research, the similarity search algorithms are provided for large video data streams. A video stream that consists of a number of frames can be expressed by a sequence in the multidimensional data space, by representing each frame with a multidimensional vector. By analyzing various characteristics of the sequence, it is partitioned into multiple video segments and clusters which are represented by hyper-rectangles. Using the hyper-rectangles of video segments and clusters, similarity functions between two video streams are defined, and two similarity search algorithms are proposed based on the similarity functions : algorithms by hyper-rectangles and by representative frames. The former is an algorithm that guarantees the correctness while the latter focuses on the efficiency with a slight sacrifice of the correctness. Experiments on different types of video streams and synthetically generated stream data show the strength of our proposed algorithms.


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Cite this article
[IEEE Style]
S. L. Lee, "Similarity Search Algorithm Based on Hyper-Rectangular Representation of Video Data Sets," The KIPS Transactions:PartD, vol. 11, no. 4, pp. 823-834, 2004. DOI: 10.3745/KIPSTD.2004.11.4.823.

[ACM Style]
Seok Lyong Lee. 2004. Similarity Search Algorithm Based on Hyper-Rectangular Representation of Video Data Sets. The KIPS Transactions:PartD, 11, 4, (2004), 823-834. DOI: 10.3745/KIPSTD.2004.11.4.823.