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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2011 - 15

Codebook Generation Using Partition and Agglomerative Clustering

CHANG, C.-T. See more information about CHANG, C.-T. on SCOPUS See more information about CHANG, C.-T. on IEEExplore See more information about CHANG, C.-T. on Web of Science, LAI, J. Z. C. See more information about  LAI, J. Z. C. on SCOPUS See more information about  LAI, J. Z. C. on SCOPUS See more information about LAI, J. Z. C. on Web of Science, JENG, M.-D. See more information about JENG, M.-D. on SCOPUS See more information about JENG, M.-D. on SCOPUS See more information about JENG, M.-D. on Web of Science
 
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Download PDF pdficon (3,737 KB) | Citation | Downloads: 1,199 | Views: 4,378

Author keywords
codebook generation, agglomerative clustering, vector quantization

References keywords
image(14), vector(11), quantization(8), processing(7), classified(6), fast(5), liaw(4)
No common words between the references section and the paper title.

About this article
Date of Publication: 2011-08-31
Volume 11, Issue 3, Year 2011, On page(s): 91 - 98
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.03015
Web of Science Accession Number: 000296186700015
SCOPUS ID: 80055065433

Abstract
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In this paper, we present a codebook generation algorithm to produce a codebook with lower distortion. Our method combines a fast codebook generation algorithm (CGAUCD) with doubling technique and fast agglomerative clustering algorithm (FACA) to generate a codebook with less computing time and lower distortion. Instead of using FACA directly to divide training vectors into M clusters, our proposed method first generates qM clusters from these training vectors, where q>1 is an integer, and then applies FACA to merge these qM clusters into M cells. This is due to the computational complexity of CGAUCD with doubling technique is less than that of FACA. These M cluster centers are used as the initial codebook for CGAUCD. Using three real images as the training set, our method can reduce the MSE and computing time of FPNN+CGAUCD, which is the available best method to our knowledge, by 0.19 to 0.38 and 74.6% to 84.3%, respectively.


References | Cited By  «-- Click to see who has cited this paper

[1] Y. C. Liaw, J. Z. C. Lai, and Winston Lo, "Image restoration of compressed image using classified vector quantization," Pattern Recognition, vol. 35, no. 2, pp. 181-192, February 2002.

[2] M. Bi , S. H. Ong, and Y. H. Ang , "Wavelet-based image compression using classified interpolative vector quantization," Optical engineering, vol. 41, pp. 1528-1535, June 2002.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 3]


[3] J. Z. C. Lai, Y. C. Liaw, and Winston Lo, "Artifact reduction of JPEG coded images using mean-removed classified vector quantization," Signal Processing, vol. 82, no. 10, pp. 1375-1388, October 2002.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 34]


[4] S. H Hong, R. H Park, S. Yang, and J. Y. Kim, "Image interpolation using interpolative classified vector quantization," Image and Vision Computing, vol. 26, no. 2, pp. 228-239, February 2008,
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 20]


[5] Y. L. Huang and R. F. Chang, "A new side-match finite-state vector quantization for image coding," Journal of Visual Communication and Image Representation, vol. 13, no. 3, pp. 335-347, September 2002.

[6] S. B. Yang and L. Y. Tseng, "Smooth side-match classified vector quantizer with variable block size," IEEE Transactions on Image Processing, vol. 10, no. 5, pp. 677-685, May 2001,
[CrossRef] [SCOPUS Times Cited 23]


[7] J. Z. C. Lai and Chen C. C. Chen, "Algorithms of halftoning color images with edge enhancement," Journal of Visual Communication and Image Representation, vol. 14, no. 4, December 2003, pp.389-404.

[8] J. Z. C. Lai and J. Y. Yen, "Inverse error-diffusion using classified vector quantization," IEEE Trans. on Image Processing, vol. 7, no. 12, pp. 1753-1758, December 1998,
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 34]


[9] P. C. Chang, C. S. Yu, and T. H. Lee, "Hybrid LMS-MMSE inverse halftoning technique," IEEE Trans. on Image Processing, vol. 10, no. 1, pp. 95-103, January 2001,
[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 65]


[10] Gersho and R. M. Gray, Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston MA., 1991.

[11] Y. Linde, A. Buzo, and R. M. Gray, "An algorithm for vector quantizer design," IEEE Trans. on Communications, vol. 28, no. 1, pp. 84-95, January 1980,
[CrossRef] [Web of Science Times Cited 4179] [SCOPUS Times Cited 5800]


[12] J. Z. C. Lai and C. C. Lue, "Fast search algorithms for VQ codebook generation," Journal of Visual Communication and Image Representation, vol. 7, no. 2, pp. 163-168, June 1996.

[13] J. Shanbehzadeh and P. O. Ogunbona, "On the computational complexity of the LBG and PNN algorithm," IEEE Trans. on Image Processing, vol. 6, no. 4, pp. 614-616, April 1997,
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 37]


[14] P. Fränti, O. Virmajoki, and Ville Hautamäki, "Fast agglomerative clustering using a k-nearest neighbor graph," IEEE Trans. on PAMI, vol. 26, no. 11, pp1875-1881, November 2006,
[CrossRef] [Web of Science Times Cited 206] [SCOPUS Times Cited 261]


[15] T. Kaukoranta, P. Fränti, and O. Nevalainen, "A fast Exact GLA based code vector activity detection," IEEE Trans. on Image Processing, vol. 9, no. 8, pp. 1337-1342, August 2000,
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 63]


[16] Jim Z. C. Lai, Y. C. Liaw, and Julie Liu, "A fast VQ codebook generation using codeword displacement," Pattern Recognition, vol. 41, no. 1, pp. 315-319, January 2008.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 63]


[17] Jim Z. C. Lai, Y. C. Liaw, and Julie Liu, "Fast k-nearest-neighbor search based on projection and triangular inequality," Pattern Recognition, vol. 40, no. 2, pp. 351-359, February 2007.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 33]


[18] W. H. Equitz, "A new vector quantization clustering algorithm," IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. 37, no. 10, pp.1568-1575, October 1989,
[CrossRef] [Web of Science Times Cited 243] [SCOPUS Times Cited 309]


References Weight

Web of Science® Citations for all references: 4,886 TCR
SCOPUS® Citations for all references: 6,745 TCR

Web of Science® Average Citations per reference: 271 ACR
SCOPUS® Average Citations per reference: 375 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-06-12 19:38 in 90 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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