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Codebook Generation Using Partition and Agglomerative ClusteringCHANG, C.-T. , LAI, J. Z. C. , JENG, M.-D. |
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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
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. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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