Abstract
One of the most serious problems in vector quantization is the high computational complexity at the encoding phase. This paper presents a new fast search algorithm for vector quantization based on Extended Associative Memories (FSA-EAM). In order to obtain the FSA-EAM, first, we used the Extended Associative Memories (EAM) to create an EAM-codebook applying the EAM training stage to the codebook produced by the LBG algorithm. The result of this stage is an associative network whose goal is to establish a relation between training set and the codebook generated by the LBG algorithm. This associative network is EAM-codebook which is used by the FSA-EAM. The FSA-EAM VQ process is performed using the recalling stage of EAM. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantage offered by the proposed algorithm is high processing speed and low demand of resources (system memory), while the encoding quality remains competitive.
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Gray, R.M.: Vector Quantization. IEEE ASSP Magazine 1, 4–9 (1984)
Nasrabadi, N.M., King, R.A.: Image Coding Using Vector Quantization: A Review. IEEE Trans. on Communications 36(8), 957–971 (1988)
Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer, Norwell (1992)
Linde, Y., Buzo, A., Gray, R.: An Algorithm for Vector Quantizer Design. IEEE Trans. on Communications 28(1), 84–95 (1980)
Lloyd, L.P.: Least Squares Quantization in PCM. IEEE Trans. Inform. Theory IT-28, 129–137 (1982)
Bahram, S., Azami, Z., Feng, G.: Robust Vector Quantizer Design Using Competitive Learning Neural Networks. In: Proc. of European Workshop on Emerging Techniques for Communications Terminals, pp. 72–75. IEEE Press, Toulouse (1997)
Kohonen, T.: Automatic Formation of Topological Maps of Patterns in a Self organizing System. In: Oja, E., Simula, O. (eds.) Proc. 2SCIA, Scand. Conf. on Image Analysis, pp. 214–220. Helsinki, Finland (1981)
Kohonen, T.: The Self-Organizing Map. IEEE Proc. 78(9), 1464–1480 (1990)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
Nasrabadi, N., Feng, Y.: Vector Quantization of Images based upon the Kohonen Self-Organizing Feature Maps. In: IEEE International Conference on Neural Networks, vol. 1, pp. 101–108 (1988)
Amerijckx, C., Verleysen, M., Thissen, P., Legat, J.-D.: Image Compression by Self-Organized Kohonen Map. IEEE Trans. on Neural Networks 9, 503–507 (1998)
Amerijckx, C., Legat, J.-D., Verleysen, M.: Image Compression Using Self-Organizing Maps. Systems Analysis Modelling Simulation 43(11), 1529–1543 (2003)
Guzmán, E., Pogrebnyak, O., Yañez, C.: Design of an Evolutionary Codebook Based on Morphological Associative Memories. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 601–611. Springer, Heidelberg (2007)
Sossa, H., Barrón, R., Vázquez, A.: Real-valued Patterns Classification based on Extended Associative Memory. In: Fifth Mexican International Conference on Computer Science, ENC 2004, pp. 213–219. IEEE Computer Society, México (2004)
Barron, R.: Associative Memories and Morphological Neural Networks for Patterns Recall (in Spanish). PhD Thesis, Center for Computing Research (2005)
Steinbuch, K.: Die Lernmatrix. Kybernetik 1(1), 26–45 (1961)
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Guzmán, E., Pogrebnyak, O., Fernández, L.S., Yáñez-Márquez, C. (2008). A Fast Search Algorithm for Vector Quantization Based on Associative Memories. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_60
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DOI: https://doi.org/10.1007/978-3-540-85920-8_60
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