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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)
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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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