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JCR Impact Factor: 0.595
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Issues per year: 4
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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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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
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2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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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
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (3,737 KB) | Citation | Downloads: 709 | Views: 2,482

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 1] [SCOPUS Times Cited 2]


[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 21] [SCOPUS Times Cited 31]


[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 14] [SCOPUS Times Cited 17]


[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 20]


[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 20] [SCOPUS Times Cited 32]


[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 45] [SCOPUS Times Cited 56]


[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 3476] [SCOPUS Times Cited 4665]


[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 26] [SCOPUS Times Cited 33]


[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 49] [SCOPUS Times Cited 75]


[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 34] [SCOPUS Times Cited 50]


[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 26] [SCOPUS Times Cited 48]


[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 22] [SCOPUS Times Cited 27]


[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 218] [SCOPUS Times Cited 246]


References Weight

Web of Science® Citations for all references: 3,952 TCR
SCOPUS® Citations for all references: 5,302 TCR

Web of Science® Average Citations per reference: 220 ACR
SCOPUS® Average Citations per reference: 295 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 2017-08-16 22:01 in 87 seconds.




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


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