Article

Performance analysis of using wavelet transform in content based video retrieval system

Authors:

Fang ChenAuthors Info & Claims

CEA'07: Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications

Pages 277 - 282

Published: 01 January 2007 Publication History

Abstract

Multimedia Retrieval is one of the most important and fastest growing research areas in the field of multimedia technology. Large collections of scientific, artistic and commercial data comprising image, text, audio and video abound in the present information based society. There must be an effective and precise method of assisting users to search, browse and interact with these collections. This paper has proposed and implemented a content based video retrieval system using Haar wavelet, Daubechies D4 wavelet transform and five different types of clustering techniques. The experimental results show that the Haar wavelet with 3- Level transform can perform best with a high retrieval accuracy rate (89%).

References

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{2} A. Pentland, R. W. Picard, and S. Sclaroff, "Photobook: Content-based manipulation of image databases, In B. Furht, editor, Multimedia Tools and applications, Chapter 2, pp. 43-80. Kluwer Academic Publisher, 1996.

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{3} J. R. Smith, "Integrated Spatial and Feature Image Systems: Retrieval, Analysis and Compression. PhD thesis, Columbia University, 1997.

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{4} Eric W. Wesisstein, "K-Means Clustering Algorithm" http://mathworld.wolfram.com/KMeansClusteringAlogirthm.html

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{5} W. W. Weisstein, "Harr Function", http://mathworld.wolfram.com/HaarFunction.html

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{6} J. J. Benedetto, M. Frazier, "Wavelet: Mathematics and Applications Boca Raton, FL: CRC Press 1994.

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Published In

CEA'07: Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications

January 2007

580 pages

ISBN:9789608457584

Editors:
Dharmendra Sharma
School of Information Sciences and Engineering, University of Canberra, Australia
,
L. C. Jain
School of Electrical and Information Engineering, University of South Australia, Adelaide, Australia
,
Dat Tran
School of Information Sciences & Engineering, University of Canberra, Australia
,
Wanli Ma
School of Information Sciences and Engineering, University of Canberra, Australia

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World Scientific and Engineering Academy and Society (WSEAS)

Stevens Point, Wisconsin, United States

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Published: 01 January 2007

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Content -- Based Image Retrieval Using the Dual-Tree Complex Wavelet Transform

Combining curvelet transform and wavelet transform for image denoising

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