Uma Alternativa de Aceleração do Algoritmo Fuzzy K-Means Aplicado à Quantização Vetorial

Francisco Madeiro, Rodrigo Regis de Almeida Galvão, Felipe A. B. S. Ferreira, Daniel Carvalho da Cunha

Abstract


Compressão de sinais, marca d´água digital e reconhecimento de padrões são exemplos de aplicações de quantização vetorial (QV). Um problema relevante em QV é o projetode dicionários. Neste trabalho, é apresentada uma alternativa de aceleração do algoritmo fuzzyK-Means aplicado ao projeto de dicionários. Resultados de simulações envolvendo QV de imagens e de sinais com distribuição de Gauss-Markov mostram que o método proposto leva a um aumento da velocidade de convergência (redução do número de iterações) do algoritmo fuzzy K-Means sem comprometimento da qualidade dos dicionários projetados.

References


F. Madeiro, W.T.A. Lopes, Introdução à Compressão de Sinais, Revista de Tecnologia de Informação e Comunicação, 1 (2011), 33-40.

A. Gersho, R.M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Publishers, Boston, 1992.

C.R.B. Azevedo, E.L.B. Junior, T.A.E. Ferreira, F. Madeiro, M.S. Alencar, An Evolutionary Approach for Vector Quantization Codebook Optimization, Lectures Notes in Computer Science, 5263 (2008), 452-461.

F. Madeiro,W.T.A. Lopes, M.S.Alencar, B. G. Aguiar Neto, Construção de Dicionários Voltados para a Redução da Complexidade Computacional da Etapa de Codificação da Quantização Vetorial, em “Anais do VI Congresso Brasileiro de Redes Neurais (CBRN’03)", São Paulo-SP, (2003), 439-444.

E.A. Lima, G.G.M. Melo, W.T.A. Lopes, M.S.Alencar, F. Madeiro, Um Novo Algoritmo para Atribuição de Índices: Avaliação em Quantização Vetorial de Imagem, TEMA: Tendências em Matemática Aplicada e Computacional, 10 (2009), 167-177.

C.-C. Chang, C.-Y. Lin, Y.-P. Hsieh, Data Hiding for Vector Quantization Images using Mixed-Base Notation and Dissimilar Patterns without Loss of Fidelity, Information Sciences, (2012).

C. Chang et. al., Reversible Information Hiding for VQ Indices Based on Locally Adaptive Coding, Journal of Visual Communication and Image Representation, 20:1 (2009), 57-64.

W. Chen, M. Wang, A Fuzzy C-Means Clustering-Based Fragile Watermarking Scheme for Image Authentication, Expert Systems and Applications, 36:2 (2009), 1300-1307.

C. Lin, S. Chen, N. Hsueh, Adaptive Embedding Techniques for VQ-Compressed Images, Information Sciences, 179:1-2 (2009), 140-149.

A. Srinivasan, Speaker Identification and Verification using Vector Quantization and Mel Frequency Cepstral Coefficients, Engineering and Technology, 4:1 (2012), 33-40.

R.T. Vieira, N. Brunet, S.C. Costa, S. Correia, B.G. Aguiar Neto, J.M. Fechine, Combining Entropy Measurements and Cepstral Analysis for Pathological Voice Assessment, Journal of Medical and Biological Engineering (Article in press), (2012).

Y. Linde, A. Buzo, R. M. Gray, An Algorithm for Vector Quantizer Design, IEEE Trans. Commun., 28:1 (1980), 84-95.

G.E. Tsekouras, A Fuzzy Vector Quantization Approach to Image Compression, Applied Math. and Computation, 167:1 (2005), 539-560.

E.L.Bispo Junior, C.R.B. Azevedo,W.T.A. Lopes, M.S.Alencar, F.Madeiro, Methods to Accelerate a Competitive Learning Algorithm Applied to VQ Codebook, TEMA: Tendências em Matemática Aplicada e Computacional, 11:3 (2010), 193-203.

C.R.B. Azevedo, F.E.A.G. Azevedo, W.T.A. Lopes, F. Madeiro, Terrain-Based Memetic Algorithms to Vector Quantization Design, in Nature Inspired Cooperative Strategies for Optimization (NICSO 2008), edited by N. Krasnogor, vol. 236 of Studies in Computational Intelligence, chapter 17, pp. 197-211. Springer-Verlag, Berlin, 1st edition, (2009), ISBN 978-3-642-03210-3.

C.Y. Chang, D.-F. Zhuang, A Fuzzy-Based Learning Vector Quantization Neural Network for Recurrent Nasal Papilloma Detection, IEEE Trans. Circuits Syst. I, 54:12 (2007), 2619-2627.

A.M. Filippi, J.R. Jensen, Effect of Continuum Removal on Hyperspectral Coastal Vegetation Classification using a Fuzzy Learning Vector Quantizer, IEEE Trans. Geosci. Remote Sensing, 45:6 (2007), 1857-1869.

N. Gkalelis, A. Tefas, I. Pitas, Combining Fuzzy Vector Quantization with Linear Discriminant Analysis for Continuous Human Movement Recognition, IEEE Trans. Circuits Syst. for Video Technol., 18:11 (2008), 1511-1521.

T. Phanprasit, T. Leauhatong, C. Pintavirooj, M. Sangworasil, Image Coding using Vector Quantization Based on Wavelet Transform Fuzzy C-Means and Principle Component Analysis, em “Proc. of the 9th Int. Symp. on Commun. and Information Technol. (ISCIT’2009)", (2009).

S.-T. Pan, S.-F. Liang, T.-P. Hong, J.H. Zeng, Apply Fuzzy Vector Quantization to Improve the Observation-Based Discrete Hidden Markov Model-An Example on Electroencephalogram (EEG) Signal Recognition, em “Proc. of the 2011 IEEE Int. Conf. on Fuzzy Systems", (2011).

R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, Wiley-Interscience, 2001.

R. Xu, D. Wunsch, Survey of Clustering Algorithms, IEEE Trans. Neural Networks, 16:3 (2005), 645-678.

N.B. Karayiannis, P.-I. Pai, Fuzzy Vector Quantization Algorithms and Their Applications in Image Compression, IEEE Trans. Image Processing, 4:9 (1995), 1193-1201.

J.C. Bezdek, Pattern Recognition with Objective Function Algorithms, Plenum, New York, 1981.

D. Lee, S. Baek, K. Sung, Modified K-Means Algorithm for Vector Quantizer Design, IEEE Signal Processing Lett., 4:1 (1997), 2-4.

K.K. Paliwal, V. Ramasubramanian, Comments on Modified K-Means Algorithm for Vector Quantizer Design, IEEE Trans. Image Processing, 9:11 (2000), 1964-1967.

R.M. Gray, Y. Linde, Vector Quantizers and Predictive Quantizers for Gauss-Markov Sources, IEEE Trans. Commun., 30:2 (1982), 381-389.

C.R.B. Azevedo, T.A.E. Ferreira,W.T.A. Lopes, F.Madeiro, Improving Image Vector Quantization with a Genetic Accelerated K-Means Algorithm, in Advanced Concepts for Intelligent Vision Systems, vol. 5259/2008 of Lecture Notes in Computer Science, pp. 67-76, Springer Berlin/Heidelberg, (2008).




DOI: https://doi.org/10.5540/tema.2012.013.02.0193

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.


Trends in Computational and Applied Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)

 

Indexed in:

                       

         

 

Desenvolvido por:

Logomarca da Lepidus Tecnologia