Abstract
In this paper, vector quantizer optimization is accomplished by a hybrid evolutionary method, which consists of a modified genetic algorithm (GA) with a local optimization module given by an accelerated version of the K-means algorithm. Simulation results regarding image compression based on VQ show that the codebooks optimized by the proposed method lead to reconstructed images with higher peak signal-to-noise ratio (PSNR) values and that the proposed method requires fewer GA generations (up to 40%) to achieve the best PSNR results produced by the conventional GA + standard K-means approach. The effect of increasing the number of iterations performed by the local optimization module within the proposed method is discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic Publishers, Boston (1992)
Louis, J.S., Johnson, J.: Solving Similar Problems Using Genetic Algorithms and Case-Based Memory. In: Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA 1997), pp. 283–290. Morgan Kaufmann, San Francisco (1997)
Kohonen, T.: Self-Organizing Maps. Springer, Berlin (2001)
Bei, C.D., Gray, R.M.: An Improvement of the Minimum Distortion Encoding Algorithm for Vector Quantization. IEEE Trans. on Communications 33, 1132–1133 (1985)
Linde, Y., Buzo, A., Gray, R.M.: An Algorithm for Vector Quantizer Design. IEEE Trans. on Communications 28, 84–95 (1980)
Lee, D., Baek, S., Sung, K.: Modified K-means Algorithm for Vector Quantizer Design. IEEE Signal Processing Lett. 4, 2–4 (1997)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)
Krishna, K., Murty, M.: Genetic K-means Algorithm. IEEE Trans. on Systems, Man and Cybernetics 29, 433–439 (1999)
Fränti, P.: Genetic Algorithm with Deterministic Crossover for Vector Quantization. Pattern Recog. Lett. 21, 61–68 (2000)
Ng, W., Choi, S., Ravishankar, C.: An Evolutionary Approach for Vector Quantizer Design. In: Proceedings of the Second IEEE International Conference on Evolutionary Computing, pp. 406–411 (1995)
Smith, J.: On Replacement Strategies in Steady State Evolutionary Algorithms. Evol. Comput. 15, 29–59 (2007)
Leung, F., Lam, H., Ling, S., Tam, P.: Tunning of the Structure and Parameters of a Neural Network Using an Improved Genetic Algorithm. IEEE Trans. on Neural Networks 14, 79–88 (2003)
Gordon, V.S., Pirie, R., Wachter, A., Sharp, S.: Terrain-Based Genetic Algorithm (TBGA): Modeling Parameter Space as Terrain. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 229–235. Morgan Kaufmann, Orlando (1999)
Julstrom, B.A.: Greedy, Genetic and Greedy Genetic Algorithms for the Quadratic Knapsack Problem. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 607–614. ACM, Washington (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Azevedo, C.R.B., Ferreira, T.A.E., Lopes, W.T.A., Madeiro, F. (2008). Improving Image Vector Quantization with a Genetic Accelerated K-Means Algorithm. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-88458-3_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88457-6
Online ISBN: 978-3-540-88458-3
eBook Packages: Computer ScienceComputer Science (R0)