JACIII Vol.12 No.3 pp. 268-276
doi: 10.20965/jaciii.2008.p0268


An Efficient DCT-Based Image Retrieval Approach Using Distance Threshold Pruning

Tienwei Tsai*, Te-Wei Chiang**, and Yo-Ping Huang***

* Department of Information Management, Chihlee Institute of Technology

** Department of Accounting Information Systems, Chihlee Institute of Technology, No.313, Sec.1, Wunhua Rd., Banciao City, Taipei County 220, Taiwan

*** Department of Electrical Engineering, National Taipei University of Technology, 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 106, Taiwan

April 13, 2007
September 12, 2007
May 20, 2008
content-based image retrieval, discrete cosine transform, color space, distance threshold pruning

Content-based image retrieval (CBIR) techniques would allow indexing and retrieving images based on their low-level contents, which involves a large number of image pixels and thus becomes an inherently and essentially computational intensive task. This paper proposes a distance threshold pruning (DTP) method to alleviate computational burden of CBIR without sacrificing its accuracy. In our approach, the images are converted into the YUV color space, and then transformed into discrete cosine transform (DCT) coefficients. Benefited from the energy compacting property of DCT, Only the low-frequency DCT coefficients of Y, U, and V components are stored. On querying an image, at the first stage, the DTP serves as a filter to remove those candidates with widely distinct features. At the second stage, the detailed similarity comparison (DSC) is performed on those remaining candidates passing through the first stage. The experimental results show that both high efficacy and high data reduction rate can be achieved simultaneously by using the proposed approach.

Cite this article as:
T. Tsai, T. Chiang, and Y. Huang, “An Efficient DCT-Based Image Retrieval Approach Using Distance Threshold Pruning,” J. Adv. Comput. Intell. Intell. Inform., Vol.12, No.3, pp. 268-276, 2008.
Data files:
  1. [1] V. Gudivada and V. Raghavan, “Content-Based Image Retrieval Systems,” IEEE Computers, 28-9, pp. 18-22, 1995.
  2. [2] N. Ahmed, T. Natarajan, and K. R. Rao, “Discrete Cosine Transform,” IEEE Trans. on Computers, 23, pp. 90-93, 1974.
  3. [3] R. Datta, J. Li, and J. Z. Wang, “Content-Based Image Retrieval – Approaches and Trends of the New Age,” Proc. of Int. Workshop on Multimedia Information Retrieval, ACM, pp. 253-262, 2005.
  4. [4] V. Castelli and L. D. Bergman, “Image Databases,” John Wiley & Sons, New York, 2002.
  5. [5] T. Randen and J. H. Husoy, “Filtering for Texture Classification: A Comparative Study,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 21-4, pp. 291-310, 1999.
  6. [6] G. Wallace, “The JPEG Still Picture Compression Standard,” Communications of the ACM, 34-4, pp. 30-44, 1991.
  7. [7] S. Aramvith and M. T. Sun, “MPEG-1 and MPEG-2 Video Standards,” in Handbook of Image and Video Processing (A. Bovik, ed.), Academic Publishers, pp. 597-610, 2000.
  8. [8] H. J. Bae and S. H. Jung, “Image Retrieval Using Texture Based on DCT,” Proc. of Int. Conf. on Information, Communications and Signal Processing, Singapore, pp. 1065-1068, 1997.
  9. [9] T. Tsai, Y. P. Huang, and T. W. Chiang, “Fast Image Retrieval Using Low Frequency DCT Coefficients,” Proc. of the 10th Conf. on Artificial Intelligence and Applications, 2005.
  10. [10] R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, “Probability and Statistics for Engineers and Scientists,” 7th Ed., Upper Saddle River, N. J., Prentice Hall, 2002.
  11. [11] A. D. Bimbo, “Visual Information Retrieval,” San Francisco: Morgan Kaufmann, 1999.
  12. [12] Y. Liu and X. Zhou, “A Simple Texture Descriptor for Texture Retrieval,” Proc. of Int. Conf. on Communication Technology, pp. 1662-1665, 2003.
  13. [13] R. C. Veltkamp, “Shape Matching: Similarity Measures and Algorithms,” Proc. the Int. Conf. on Shape Modeling and Applications, pp. 188-197, 2001.
  14. [14] P. W. Huang and S. K. Dai, “Design of a Two-Stage Content-Based Image Retrieval System Using Texture Similarity,” Information Processing and Management, 40-1, pp. 81-96, 2004.
  15. [15] J. Z. Wang, “Content Based Image Search Demo Page,”
    Available at˜zwang/project/imsearch/WBIIS.html
  16. [16] A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 22-12, pp. 1349-1380, 2000.

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