IMAGE SEQUENCES TEXTURE ANALYSIS BASED ON VECTOR QUANTIZATION

Authors

  • S. I. Bogucharskiy Kharkiv National University of Radio Electronics, Ukraine, Ukraine
  • S. V. Mashtalir Kharkiv National University of Radio Electronics, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2014-2-14

Keywords:

image, texture, clustering, matrix algorithm, neural networks.

Abstract

The approach for the image sequence (videodata) analysis is proposed. For this purpose, the matrix analogs of existing neural network approaches is developed. This aallows to takes into account the spatial relationships of multimedia information, and to reduce the time of information processing through the introduction of a new neural network matrix training procedures. The texture isselected as a basic characteristic for original data partition, which, in turn, improves the clustering accuracy.

References

Derin H. Modeling and segmentation of noisy and textured images using Gibbs random fields / H. Derin, H. Elliot // IEEE Trans. on Pattern Analysis and Machine Intelligence. – 1987. – Vol. 9. – P. 39–55. 2. Varma M. A statistical approach to texture classification from single images / M. Varma, A. Zisserman // Int. J. of Computer Vision. – 2005. – Vol. 62. – P. 61–81. 3. Liu L. Texture classification from random features / L. Liu, P. Fieguth // IEEE Trans. on Pattern Analysis and Machine Intelligence. – 2012. – No. 34. – P. 574–586. 4. Chen Y. Q. Statistical geometrical features for texture classification / Y. Q. Chen, M. S. Nixon, D. W. Thomas // Pattern Recognition. – 1995. – No. 28. – P. 537–552. 5. Cross G. R., Jain A.K. Markov random field texture models / G. R. Cross, A. K. Jain // IEEE Trans. on Pattern Analysis and Machine Intelligence. – 1983. – No. 5. – P. 25–39. 6. Chellapa R. Classification of textures using Gaussian – Markov random fields / R. Chellapa, S. Chatterjee // IEEE Trans. on Acoust., Speech, Signal Processing. – 1985. – No. 33. – P. 959–963. 7. Ghahramani Z. An introduction to hidden Marka models and Bayesian networks / Z. Ghahramani // Int. J. of Pattern Recognition and Artificial Intelligence. – 2001. – No. 15. – P. 9–42. 8. Beal M. J. The infinite hidden Markov model / M. J. Beal, Z. Ghahramani, C. E. Rasmussen // Neural Information Processing Systems. – 2001. – P. 577–584. 9. Neural Networks for Signal Processing / ed. B. Kosko. – New Jersey : Prentice Hall, 1992. – 399 p. 10. Rojas R. Neural Network: A Systematic Introduction / R. Rojas. – Berlin : Springer-Verlag. 1996. – 502 p. 11. Хайкин С. Нейронные сети: Полный курс / С. Хайкин. – М. : Издательский дом Вильямс, 2006. – 1104 с. 12. Raveane W. Texture classification with neural networks / W. Raveane, M.A.G. Arrieta ; eds. Omatu et. al. // Distributed Computing&Artificial Intelligence. – Berlin, Springer, 2013. – P. 305–332. 13. Caldas-Pinto J. R. Self-tuning filters and predictors for twodimensional systems / J. R. Caldas-Pinto, P. E. Wellstead // Int. J. of Control. – 1985. – Vol. 42, No. 2. – P. 457–505. 14. Wellstead P. E. Two-dimensional adaptive prediction, smoothing and filtering / P. E. Wellstead, G. R. Wagner, J. R. Caldas-Pinto // IEE Proc. – 1987. – No. 3. – P. 253–267. 15. Bodyanskiy Ye. Discrete adaptive identification and extrapolation of two-dimensional fields / Ye. Bodyanskiy, I. Pliss, V. Timofeev // Pattern Recognition and Image Analysis. – 1995. – No. 3. – P. 410–416. 16. Kohonen T. Self-organizing maps / T. Kohonen. – Berlin : Springer Verlag, 1995. – 362 p.

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Published

2014-10-20

How to Cite

Bogucharskiy, S. I., & Mashtalir, S. V. (2014). IMAGE SEQUENCES TEXTURE ANALYSIS BASED ON VECTOR QUANTIZATION. Radio Electronics, Computer Science, Control, (2). https://doi.org/10.15588/1607-3274-2014-2-14

Issue

No. 2 (2014): Radio Electronics, Computer Science, Control

Section

Neuroinformatics and intelligent systems

License

Copyright (c) 2015 S. I. Bogucharskiy, S. V. Mashtalir

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