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A Fuzzy-Soft Competitive Learning Approach for Grayscale Image Compression

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Unsupervised Learning Algorithms

Abstract

In this chapter we develop a fuzzy-set-based vector quantization algorithm for the efficient compression of grayscale still images. In general, vector quantization can be carried out by using crisp-based and fuzzy-based methods. The motivation of the current work is to provide a systematic framework upon which the above two general methodologies can effectively cooperate. The proposed algorithm accomplishes this task through the utilization of two main steps. First, it introduces a specially designed fuzzy neighborhood function to quantify the lateral neuron interaction phenomenon and the degree of the neuron excitation of the standard self-organizing map. Second, it involves a codeword migration strategy, according to which codewords that correspond to small and underutilized clusters are moved to areas that appear high probability to contain large number of training vectors. The proposed methodology is rigorously compared to other relative approaches that exist in the literature. An interesting outcome of the simulation study is that although the proposed algorithm constitutes a fuzzy-based learning mechanism, it finally obtains computational costs that are comparable to crisp-based vector quantization schemes, an issue that can hardly be maintained by the standard fuzzy vector quantizers.

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References

  1. Chang, C.C., Lin, I.C.: Fast search algorithm for vector quantization without extra look-up table using declustered subcodebooks. IEE Proc. Vis. Image Sign. Process. Lett. 7(11), 304–306 (2005)

    Google Scholar 

  2. Chen, P.Y.: An efficient prediction algorithm for image quantization. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(1), 740–746 (2004)

    Article  Google Scholar 

  3. Chen, T.-H., Wu, C.-S.: Compression-unimpaired batch-image encryption combining vector quantization and index compression. Inform. Sci. 180, 1690–1701 (2010)

    Article  Google Scholar 

  4. Horng, M.-H.: Vector quantization using the firefly algorithm for image compression. Expert Syst. Appl. 39, 1078–1091 (2012)

    Article  Google Scholar 

  5. Hu, Y.-C., Su, B.-H., Tsou, C.-C.: Fast VQ codebook search algorithm for grayscale image coding. Image Vis. Comput. 26, 657–666 (2008)

    Article  Google Scholar 

  6. Poursistani, P., Nezamabadi-pour, H., Askari Moghadam, R., Saeed, M.: Image indexing and retrieval in JPEG compressed domain based on vector quantization. Math. Comput. Modell. 57, 1005–1017 (2013)

    Article  MathSciNet  Google Scholar 

  7. Qian, S.E.: Hyper spectral data compression using a fast vector quantization algorithm. IEEE Trans. Geosci. Remote Sens. 42(8), 1791–1798 (2004)

    Article  Google Scholar 

  8. De, A., Guo, C.: An adaptive vector quantization approach for image segmentation based on SOM network. Neurocomputing 149, 48–58 (2015)

    Article  Google Scholar 

  9. Kirstein, S., Wersing, H., Gross, H.-M., Körner, E.: A life-long learning vector quantization approach for interactive learning of multiple categories. Neural Netw. 28, 90–105 (2012)

    Article  Google Scholar 

  10. Pham, T.D., Brandl, M., Beck, D.: Fuzzy declustering-based vector quantization. Pattern Recognit. 42, 2570–2577 (2009)

    Article  MATH  Google Scholar 

  11. Rizzo, F., Storer, J.A., Carpentieri, B.: Overlap and channel errors in adaptive vector quantization for image coding. Inform. Sci. 171, 125–140 (2005)

    Article  MathSciNet  Google Scholar 

  12. Villmann, T., Haase, S., Kaden, M.: Kernelized vector quantization in gradient-descent learning. Neurocomputing 147, 83–95 (2015)

    Article  Google Scholar 

  13. Yan, S.B.: Constrained-storage multistage vector quantization based on genetic algorithms. Pattern Recognit. 41, 689–700 (2008)

    Article  MATH  Google Scholar 

  14. Zhou, S.S., Wang, W.W., Zhou, L.H.: A new technique for generalized learning vector quantization algorithm. Image Vis. Comput. 24, 649–655 (2006)

    Article  Google Scholar 

  15. Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. Commun. 28(1), 84–95 (1980)

    Article  Google Scholar 

  16. Patane, G., Russo, M.: The enhanced LBG. Neural Netw. 14, 1219–1237 (2001)

    Article  Google Scholar 

  17. Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)

    Book  MATH  Google Scholar 

  18. Kohonen, T.: The self-organizing map. Neurocomputing 2, 1–6 (2003)

    MATH  Google Scholar 

  19. Uchino, E., Yano, K., Azetsu, T.: A self-organizing map with twin units capable of describing a nonlinear input-output relation applied to speech code vector mapping. Inform. Sci. 177, 4634–4644 (2007)

    Article  Google Scholar 

  20. D’Ursoa, P., De Giovanni, L., Massari, R.: Self-organizing maps for imprecise data. Fuzzy Sets Syst. 237, 63–89 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  21. Wang, C.-H., Lee, C.-N., Hsieh, C.-H.: Classified self-organizing map with adaptive subcodebook for edge preserving vector quantization. Neurocomputing 72, 3760–3770 (2009)

    Article  Google Scholar 

  22. Bezdek, J.C., Pal, N.R.: Two soft relatives of learning vector quantization. Neural Netw. 8, 729–743 (1995)

    Article  Google Scholar 

  23. Filippi, A.M., Jensen, J.R.: Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sens. Environ. 100, 512–530 (2006)

    Article  Google Scholar 

  24. Feng, H.-M., Chen, C.-Y., Ye, F.: Evolutionary fuzzy particle swarm optimization vector quantization learning scheme in image compression. Expert Syst. Appl. 32, 213–222 (2007)

    Article  Google Scholar 

  25. Hung, W.-L., Chen, D.-H., Yang, M.-S.: Suppressed fuzzy-soft learning vector quantization for MRI segmentation. Artif. Intell. Med. 52, 33–43 (2011)

    Article  Google Scholar 

  26. Karayiannis, N.B., Randolph-Gips, M.M.: Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: single-norm algorithms. IEEE Trans. Neural Netw. 16(2), 423–435 (2005)

    Article  Google Scholar 

  27. Tsekouras, G.E., Dartzentas, D., Drakoulaki, I., Niros, A.D.: Fast fuzzy vector quantization. In: Proceedings of IEEE International Conference on Fuzzy Systems, Barcelona, Spain (2010)

    Google Scholar 

  28. Tsolakis, D., Tsekouras, G.E., Tsimikas, J.: Fuzzy vector quantization for image compression based on competitive agglomeration and a novel codeword migration strategy. Eng. Appl. Artif. Intell. 25, 1212–1225 (2011)

    Article  Google Scholar 

  29. Tsekouras, G.E.: A fuzzy vector quantization approach to image compression. Appl. Math. Comput. 167, 539–560 (2005)

    MathSciNet  MATH  Google Scholar 

  30. Tsekouras, G.E., Mamalis, A., Anagnostopoulos, C., Gavalas, D., Economou, D.: Improved batch fuzzy learning vector quantization for image compression. Inform. Sci. 178, 3895–3907 (2008)

    Article  MathSciNet  Google Scholar 

  31. Wu, K.-L., Yang, M.-S.: A fuzzy-soft learning vector quantization. Neurocomputing 55, 681–697 (2003)

    Article  Google Scholar 

  32. Tsao, E.C.K., Bezdek, J.C., Pal, N.R.: Fuzzy Kohonen clustering networks. Pattern Recognit. 27(5), 757–764 (1994)

    Article  Google Scholar 

  33. Pal, N.R., Bezdek, J.C., Hathaway, R.J.: Sequential competitive learning and the fuzzy c-means clustering algorithms. Neural Netw. 9(5), 787–796 (1996)

    Article  Google Scholar 

  34. Tsekouras, G.E., Sarimveis, H.: A new approach for measuring the validity of the fuzzy c-means algorithm. Adv. Eng. Software 35, 567–575 (2004)

    Article  MATH  Google Scholar 

  35. Tsolakis, D., Tsekouras, G.E., Niros, A.D., Rigos, A.: On the systematic development of fast fuzzy vector quantization for grayscale image compression. Neural Netw. 36, 83–96 (2012)

    Article  MATH  Google Scholar 

  36. Celebi, M.E., Kingravi, H., Vela, P.A.: A comparative study of efficient initialization methods for the K-means clustering algorithm. Expert Syst. Appl. 40(1), 200–210 (2013)

    Article  Google Scholar 

  37. Celebi, M.E., Kingravi, H.: Deterministic initialization of the K-means algorithm using hierarchical clustering. Int. J. Pattern Recognit. Artif. Intell. 26(7), 1250018 (2012)

    Article  MathSciNet  Google Scholar 

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Correspondence to Dimitrios M. Tsolakis .

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Tsolakis, D.M., Tsekouras, G.E. (2016). A Fuzzy-Soft Competitive Learning Approach for Grayscale Image Compression. In: Celebi, M., Aydin, K. (eds) Unsupervised Learning Algorithms. Springer, Cham. https://doi.org/10.1007/978-3-319-24211-8_14

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  • DOI: https://doi.org/10.1007/978-3-319-24211-8_14

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