Dynamic-segmentation-based feature dimension reduction for quick audio/video searching

We propose a new feature dimension reduction method for multimedia search. The main technique in the method is dynamic segmentation that partitions sequential feature trajectories dynamically. While dynamic segmentation reduces the average dimensionality and accelerates the search, it requires huge...

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Bibliographic Details
Published in2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698) Vol. 2; pp. II - 389
Main Authors Kimura, A., Kashino, K., Kurozumi, T., Murase, H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2003
Subjects
Acceleration
Audio databases
Electronic mail
Karhunen-Loeve transforms
Laboratories
Multimedia databases
Piecewise linear techniques
Principal component analysis
Spatial databases
Streaming media
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Summary:We propose a new feature dimension reduction method for multimedia search. The main technique in the method is dynamic segmentation that partitions sequential feature trajectories dynamically. While dynamic segmentation reduces the average dimensionality and accelerates the search, it requires huge amount of calculation. Thus, our method quickly executes suboptimal partitioning of the trajectories by using the discreteness of dimension changes. This guarantees the optimal amount of calculation to derive the suboptimal partitioning under the condition that the dimension monotonously increases as the segment length increases. The experiment shows that our method is over 10 times faster than a straightforward dynamic segmentation method.
ISBN:9780780379657
0780379659
DOI:10.1109/ICME.2003.1221635