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A Survey on use of Neuro-Cognitive and Probabilistic Paradigms in Pattern Recognition

Author Affiliations

  • 1Department of Computer Science, Abdul Wali Khan University, Mardan, PAKISTAN
  • 2 Faculty of Information Technology, University of Central Punjab, Lahore, 54500, PAKISTAN

Res. J. Recent Sci., Volume 2, Issue (4), Pages 74-79, April,2 (2013)

Abstract

The state of any system is defined by data of some sort. Data by itself is meaningless unless it is transformed into information by putting it into a meaningful context. Images are also defined by data accumulated by quantification of color intensities of pixels. Either the values of the pixels form patterns or the arrangement of pixels form patterns. Various statistical and probabilistic models have been employed by the researchers to extract interesting information from data. Also the pattern recognition problems are divided into two levels termed as generic and specific. In this piece of work we identify problems from both these domains of pattern recognition. This comprehensive survey not only provides a tutorial on the probabilistic and neuro-cognitive techniques used for this purpose but it also explores monumental issues in the domain.

References

  1. Hasti T., Tibshirani R. and Friedman J., The Elements of Statistical Learning, Springer-Verlag, (2000)
  2. Mohamadian Zahra, Image Duplication Forgery Detection using Two Robust Features, Res. J. Recent Sci., 1(12), 1-6 (2012)
  3. MacKay D.J.C., Information Theory, Inference and LearningAlgorithms, Cambridge University Press, (2003)
  4. Fam D.F., Koh S.P., Tiong S.K. and Chong K.H., Res.J.Recent Sci.,1(9), 74-78 (2012)
  5. Khan Y.D., Ahmed F. and Waqas M., Iris Recognition Using Back Propagation, World Applied Science Journal, 16(5),(2012)
  6. Tang H.L., Hanka R. and Ip H.H.S., Histological Image Retrieval Based on Semantic Content Analysis, IEEE Trans. on Info. Tech. in Biomed., 7(1), 26-36 (2003)
  7. Sudhamani M.V. and Venugopal C.R., Multidimensional Indexing Structures for Content-Based Image Retrieval: A Survey, Int. J. of Inno. Computing, Info. and Control, 4(4),867-881 (2008)
  8. Jiang W., Er G., Dai Q. and Gu J., Similarity Based Online Feature Selection in Content Based Image Retrieval, IEEE Trans. on Image Proc, 15(3), 702-712 (2006)
  9. Rahmani R., Goldman S.A., Zhang H., Choletti S.R. and Fritts J.E., Localized Content-Based Image Retrieval, IEEE Trans. on Pattern Analysis and Machine Intell., 30(11), 1902-1912 (2008)
  10. Wu K. and Yap K.H., Fuzzy S.V.M. for Content Based Image Retrieval: a psuedo-label support vector machine framework, IEEE Comput. Intelli. Mag, 1(2), 10-16 (2006)
  11. Chun Y.D, Kim N.C. and Jang I.H., Content Based Image Retrieval Using Multiresolution Color and Texture Features, IEEE Trans. on Multimedia, 10(6), 1073-1084 (2008)
  12. Marakakis A., Galatsanos N., Likas A. and Stafylopatis A., Probablistic Relevance Feedback Approach for Content-Based Image Retrieval Based on Gaussian Mixture Models, IET Image Proc., 3(1), 10-45(2009)
  13. Monro D.M., Rakshit S. and Zhang D., DCT-Based Iris Recognition, IEEE Trans.on Pat.Analysis Mach.Intell., 29(4), (2007)
  14. Abiyev R.H. and Altunkaya K., Personal Iris Recognition Using Neural Network, Int. J.of Security and its App., 2(2), (2008)
  15. Ma L., Tan T., Wang Y. and Zhang D., Personal Identification Based on Iris Texture Analysis, IEEE Trans.on Pat.Analysis Mach.Intell, 25(12) (2003)
  16. Lim S., Lee K., Byeon O. and Kim T., Efficient Iris Recognition through Improvement of Feature Vector Classifier, ETRI Journal, 23(2), (2001)
  17. Daugman J., How Iris Recognition Work, IEEE Transactions on Circuits and Systems for Video Technology, 14, (2002)
  18. Grigorova A., De Natalie F.. G.B., Dagli C. and Huang T.S., Content Based Image Retrieval by Feature Adaptation and Relevance Feedback, IEEE Trans. on Multimedia, 9(6), 1183-1192 (2007)
  19. Qiu G. and Lam K.M., Frequency Layered Color Indexing for Content Based Image Retrieval, IEEE Trans. on Image Process, 12(1), 102-103 (2003)
  20. Yap P.T. and Paramesran R., Content Based Image Retrieval using Legendre Chromaticity Distribution moments, IEE Proceed.- Vision, Image and Signal Process., 153(1), 17-24 (2006)
  21. Patheja P.S.,Waoo Akhilesh A.and Maurya Jay Prakash, An Enhanced Approch for Content Based Image Retrieval, Res. J. Recent Sci.,1(ISC-2011) 415-418 (2012)
  22. Venkatesh Y.V., Raja S.K. and Kumar A.J., On the Application of a Modified Self-Organizing Neural Network to Estimate Stereo Disparity, IEEE Trans. on Image Proc., 16(11), 2822-2829 (2007)
  23. Sao A.K. and Yegnanarayana B., Face Verification Using Template Matching, IEEE Trans.on Info. Forensics and Security, 2(3), 636-641 (2007)
  24. Lu H.C., and Tsai C.H., Image Recognition Study via the Neural Fuzzy System, Proc. Int. Conf. Intelligent Engineering Systems INES '06, 222-226 (2006)
  25. Mirza Nawazish and Saeed, Mawal Sara, Res. J. Recent Sci.,1(11), 41-46 (2012)
  26. Wasserman L, All of Nonparametric Statistics, Springer, (2007)
  27. Plaut D.C., Nowlan S.J. and Hinton G.E., Experiments on learning by back propagation, Carnegie-Mellon University, (1986)
  28. Hogg R.V., McKean J.W. and Craig A.T., Introduction to Mathematical Statistics, Pearson Education, (2005)
  29. Davidson A.C., Statistical Models, Cambridge University Press, (2003)
  30. McLachlan G.J. and Peel D., Finite Mixture Models, John Wiley and Sons (2000)
  31. 1.Lindsay B.G., Mixture Models: Theory, Geometry and Applications, IMS, (1995)
  32. Hu M., Visual pattern recognition by moment invariants, IRE Trans. on Info. Theory, 8(2), (1962)
  33. Bezdek J.C., Krisnapuram R. and Pal N.R., Fuzzy models and algorithms for pattern recognition and image processing, Springer, (2005)
  34. Jolliffe I.T., Principal Component Analysis, Series: Springer Series in Statistics, Springer (2002)
  35. Duda R. O., Hart P. E., Stork D. G., Unsupervised Learning and Clustering, Wiley, (2001)
  36. Rao Sathish U. and Rodrigues L.L. Raj, Res.J.Recent Sci.,1(5), 75-82 (2012)