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Color Object Extraction by Parallel BDSONN Architecture

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Abstract

Chapter 5 introduced the bidirectional self-organizing neural network (BDSONN) architecture [88, 241–244]. The chapter demonstrated the efficiency of the BDSONN architecture [88, 241–244] over the multilayer self-organizing neural network (MLSONN) architecture [89] both in terms of object extraction efficiency and time efficiency.

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References

  1. S. Kirkpatrik, C. Gelatt, M. Vecchi, Optimization by simulated annealing. Science 22, 671–680 (1983)

    Article  Google Scholar 

  2. T. Kohonen, Self-Organization and Associative Memory (Springer, London, 1984)

    MATH  Google Scholar 

  3. T. Kohonen, Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  4. S. Bhattacharyya, P. Dutta, XMUBET with CONSENT: a pixel hostility induced multiscale object extractor, in Proceedings of IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP’04), Melbourne, Australia, Dec 2004, pp. 277–282

    Google Scholar 

  5. S. Bhattacharyya, P. Dutta, Multiscale object extraction with MUSIG and MUBET with CONSENT: a comparative study, in Proceedings of KBCS 2004, Hyderabad, India, Dec 2004, pp. 100–109

    Google Scholar 

  6. T. Kohonen, Self-Organizing Maps. Springer Series in Information Sciences, vol. 30 (Springer, Berlin, Heidelberg, New York, 2001)

    Google Scholar 

  7. S. Bhattacharyya, U. Maulik, P. Dutta, Multilevel image segmentation with adaptive image context based thresholding. Int. J. Appl. Soft Comput. 11, 946–962 (2010)

    Article  Google Scholar 

  8. A. Ghosh, N.R. Pal, S.K. Pal, Self-organization for object extraction using a multilayer neural network and fuzziness measures. IEEE Trans. Fuzzy Syst. 1(1), 54–68 (1993)

    Article  Google Scholar 

  9. S. Bhattacharyya, P. Dutta, U. Maulik, Multi-scale object extraction using self organizing neural network with a multi-level sigmoidal activation function, in Proceedings of the Fifth International Conference on Advances in Pattern Recognition, Kolkata, India, 2003, pp. 435–438

    Google Scholar 

  10. S. Bhattacharyya, P. Dutta, U. Maulik, Self organizing neural network (SONN) based gray scale object extractor with a multilevel sigmoidal (MUSIG) activation function. Int. J. Found. Comput. Decis. Sci. 33(2), 46–50 (2008)

    Google Scholar 

  11. S. Bhattacharyya, P. Dutta, P.K. Nandi, True color object extraction by a parallel self organizing neural network (PSONN) architecture guided by XMUBET with CONSENT, in Proceedings of EAIT 2006, Kolkata, 2006, pp. 295–299

    Google Scholar 

  12. S. Bhattacharyya, K. Dasgupta, Color object extraction from a noisy background using parallel multi-layer self-organizing neural networks, in Proceedings of CSI-YITPA(E) 2003, Kolkata, India, 2003, pp. 32–36

    Google Scholar 

  13. S. Bhattacharyya, P. Dutta, U. Maulik, P.K. Nandi, Multilevel activation functions for true color image segmentation using a self supervised parallel self organizing neural network (PSONN) architecture: a comparative study. Int. J. Comput. Sci. 2(1), 9–21 (2007)

    Google Scholar 

  14. S. Bhattacharyya, P. Dutta, U. Maulik, A bi-directional self-organizing neural network architecture for object extraction from a noisy background, in Proceedings of International Conference on Optics and Optoelectronics (ICOL), Dehra Dun, India, 2005

    Google Scholar 

  15. S. Bhattacharyya, P. Dutta, U. Maulik, Binary object extraction using bi-directional self-organizing neural network (BDSONN) architecture with fuzzy context sensitive thresholding. Pattern Anal. Appl. 10, 345–360 (2007)

    Article  MathSciNet  Google Scholar 

  16. S. Bhattacharyya, P. Dutta, U. Maulik, Fuzzy context sensitive thresholding guided bidirectional self organizing neural network (BDSONN): a gray scale object extractor, in Proceedings of International Conference on Intelligent Sensing and Information Processing (ICISIP 2006), Bangalore, India, 2006, pp. 165–168

    Google Scholar 

  17. S. Bhattacharyya, P. Dutta, D. DuttaMajumder, Multiscale object extraction using a self organizing neural network with multilevel beta activation function and its sigmoidal counterpart: a comparative study, in Proceedings of International Conference on Recent Trends and New Directions of Research in Cybernetics and Systems Theory, Guwahati, India, 2004

    Google Scholar 

  18. J. Kittle, J. Illingworth, Minimum error thresholding. Pattern Recognit. 19, 41–47 (1986)

    Article  Google Scholar 

  19. J. Shi, J. Malik, Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

  20. B.J.T. Fernandes, G.D.C. Cavalcanti, T.I. Ren, Classification and segmentation of visual patterns based on receptive and inhibitory fields, in Proceedings of the 8th International Conference on Hybrid Intelligent Systems, Barcelona, 2008, pp. 126–131

    Google Scholar 

  21. J. Liu, Y. Yang, Multiresolution color image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 16, 689–700 (1994)

    Article  Google Scholar 

  22. M. Borsotti, P. Campadelli, R. Schettini, Quantitative evaluation of color image segmentation results. Pattern Recognit. Lett. 19, 741–747 (1998)

    Article  MATH  Google Scholar 

  23. H. Zhang, J. Fritts, S. Goldman, An entropy-based objective evaluation method for image segmentation, in Proceedings of SPIE Storage and Retrieval Methods and Applications for Multimedia, San Jose, 2004

    Google Scholar 

  24. S. Bhattacharyya, U. Maulik, P. Dutta, A parallel bi-directional self organizing neural network (PBDSONN) architecture for color image extraction and segmentation. Neurocomputing 86, 1–23 (2012)

    Article  Google Scholar 

  25. S. Bhattacharyya, P. Dutta, U. Maulik, P.K. Nandi, Pure color object extraction using a parallel bi-directional self-organizing neural network (PBDSONN) architecture, in Proceedings of National Conference on Recent Trends in Intelligent Computing (RTIC 2006), Kalyani, 2006, pp. 79–85

    Google Scholar 

  26. H.C. Chen, W.J. Chien, S.J. Wang, Contrast-based color image segmentation. IEEE Signal Process. Lett. 11, 641–644 (2004)

    Article  Google Scholar 

  27. S. Makrogiannis, G. Economou, S. Fotopoulos, A region dissimilarity relation that combines feature-space and spatial information for color image segmentation. IEEE Trans. Syst. Man Cybern. B 35, 44–53 (2005)

    Article  Google Scholar 

  28. S. Makrogiannis, G. Economou, S. Fotopoulos, N.G. Bourbakis, Segmentation of color images using multiscale clustering and graph theoretic region synthesis. IEEE Trans. Syst. Man Cybern. A 35, 224–238 (2005)

    Article  Google Scholar 

  29. D. Comaniciu, P. Meer, Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1–18 (2002)

    Article  Google Scholar 

  30. T. Wenbing, J. Hai, Z. Yimin, Color image segmentation based on mean shift and normalized cuts. IEEE Trans. Syst. Man Cybern. B 37, 1382–1389 (2007)

    Google Scholar 

  31. Q. Luo, T.M. Khoshgoftaar, Unsupervised multiscale color image segmentation based on MDL principle. IEEE Trans. Image Process. 15, 2755–2761 (2006)

    Article  Google Scholar 

  32. S.Z. Li, Markov Random Field Modeling in Computer Vision (Springer, London, 2001)

    Book  Google Scholar 

  33. M.V. Ibanez, A. Simo, Parameter estimation in Markov random field image modeling with imperfect observations: a comparative study. Pattern Recognit. Lett. 24(14), 2377–2389 (2003)

    Article  MATH  Google Scholar 

  34. M. Robinson, M. Azimi-Sadjadi, J. Salazar, A temporally adaptive classifier for multispectral imagery. IEEE Trans. Neural Netw. 15(1), 159–165 (2004)

    Article  Google Scholar 

  35. J.L. Marroquin, E.A. Santana, S. Botello, Hidden Markov measure field models for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 25(11), 1380–1387 (2003)

    Article  Google Scholar 

  36. G. Celeux, F. Forbes, N. Peyrard, EM procedures using mean field-like approximations for Markov model-based image segmentation. Pattern Recognit. 36(1), 131–144 (2003)

    Article  MATH  Google Scholar 

  37. A. Diplaros, N. Vlassis, T. Gevers, A spatially constrained generative model and an EM algorithm for image segmentation. IEEE Trans. Neural Netw. 18, 798–808 (2007)

    Article  Google Scholar 

  38. Y. Zhang, M. Brady, S. Smith, Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001)

    Article  Google Scholar 

  39. Q. Jackson, D.A. Landgrebe, Adaptive Bayesian contextual classification based on Markov random fields. IEEE Trans. Geosci. Remote Sens. 40, 2454–2463 (2002)

    Article  Google Scholar 

  40. M. Egmont-Petersen, D. de Ridder, H. Handels, Image processing with neural networks: a review. Pattern Recognit. 35, 2279–2301 (2002)

    Article  MATH  Google Scholar 

  41. G.L. Foresti, F.A. Pellegrino, Automatic visual recognition of deformable objects for grasping and manipulation. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 34, 325–333 (2004)

    Article  Google Scholar 

  42. S.H. Ong, N.C. Yeo, K.H. Lee, Y.V. Venkatesh, D.M. Cao, Segmentation of color images using a two-stage self-organizing network. Image Vis. Comput. 20, 279–289 (2002)

    Article  Google Scholar 

  43. Y. Jiang, K.J. Chen, Z.H. Zhou, SOM based image segmentation, in Proceedings of 9th Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Chongqing, Japan, ed. by G. Wang, Q. Liu, Y. Yao, A. Skowron. Lecture Notes in Artificial Intelligence, vol. 2639, 2003, pp. 640–643

    Google Scholar 

  44. Y. Jiang, Z.H. Zhou, SOM ensemble-based image segmentation. Neural Process. Lett. 20, 171–178 (2004)

    Article  Google Scholar 

  45. M. Sezgin, B. Sankur, Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13, 146–168 (2004)

    Article  Google Scholar 

  46. N. Papamarkos, C. Strouthopoulos, I. Andreadis, Multithesholding of colour and gray-level images through a neural network technique. Image Vis. Comput. 18, 213–222 (2000)

    Article  Google Scholar 

  47. H.S. Hosseini, R. Safabakhsh, Automatic multilevel thresholding for image segmentation by the growing time adaptive self-organizing map. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1388–1393 (2002)

    Article  Google Scholar 

  48. A. Nakib, H. Oulhadj, P. Siarry, Image histogram thresholding based on multiobjective optimization. Signal Process. 87, 2516–2534 (2007)

    Article  MATH  Google Scholar 

  49. A. Tremeau, P. Colantoni, Regions adjacency graph applied to color image segmentation. IEEE Trans. Image Process. 9, 735–744 (2000)

    Article  Google Scholar 

  50. S.C. Cheng, C.K. Yang, A fast and novel technique for color quantization using reduction of color space dimensionality. Pattern Recognit. Lett. 22(8), 845–856 (2001)

    Article  MATH  Google Scholar 

  51. Y. Sirisathitkul, S. Auwatanamongkol, B. Uyyanonvara, Color image quantization using distances between adjacent colors along the color axis with highest color variance. Pattern Recognit. Lett. 25, 1025–1043 (2004)

    Article  Google Scholar 

  52. G. Dong, M. Xie, Color clustering and learning for image segmentation based on neural networks. IEEE Trans. Neural Netw. 16, 925–936 (2005)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Kolkata, India

    Siddhartha Bhattacharyya

  2. Department of Computer Science & Engineering, Jadavpur University, Kolkata, India

    Ujjwal Maulik

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  1. Siddhartha Bhattacharyya
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  2. Ujjwal Maulik
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Bhattacharyya, S., Maulik, U. (2013). Color Object Extraction by Parallel BDSONN Architecture. In: Soft Computing for Image and Multimedia Data Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40255-5_7

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  • DOI: https://doi.org/10.1007/978-3-642-40255-5_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40254-8

  • Online ISBN: 978-3-642-40255-5

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