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Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design

Center of Science and Technology, Catholic University of Pernambuco (UNICAP), Recife 50050-900, Brazil
Centro de Informática, Universidade Federal de Pernambuco (UFPE), Recife 50740-560, Brazil
Department of Electrical Engineering, Center of Alternative and Renewable Energy, Federal University of Paraíba (UFPB), João Pessoa 58038-130, Brazil
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Sensors 2016, 16(11), 1963;
Received: 24 August 2016 / Revised: 11 November 2016 / Accepted: 15 November 2016 / Published: 23 November 2016
(This article belongs to the Section Physical Sensors)
PDF [2632 KB, uploaded 23 November 2016]


The performance of signal processing systems based on vector quantization depends on codebook design. In the image compression scenario, the quality of the reconstructed images depends on the codebooks used. In this paper, alternatives are proposed for accelerating families of fuzzy K-means algorithms for codebook design. The acceleration is obtained by reducing the number of iterations of the algorithms and applying efficient nearest neighbor search techniques. Simulation results concerning image vector quantization have shown that the acceleration obtained so far does not decrease the quality of the reconstructed images. Codebook design time savings up to about 40% are obtained by the accelerated versions with respect to the original versions of the algorithms. View Full-Text
Keywords: fuzzy K-means; vector quantization; computational complexity fuzzy K-means; vector quantization; computational complexity

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Mata, E.; Bandeira, S.; De Mattos Neto, P.; Lopes, W.; Madeiro, F. Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design. Sensors 2016, 16, 1963.

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