Multilevel Activation Functions For True Color Image Segmentation Using a Self Supervised Parallel Self Organizing Neural Network (PSONN) Architecture: A Comparative Study
The paper describes a self supervised parallel self organizing neural network (PSONN) architecture for true color image segmentation. The proposed architecture is a parallel extension of the standard single self organizing neural network architecture (SONN) and comprises an input (source) layer of image information, three single self organizing neural network architectures for segmentation of the different primary color components in a color image scene and one final output (sink) layer for fusion of the segmented color component images. Responses to the different shades of color components are induced in each of the three single network architectures (meant for component level processing) by applying a multilevel version of the characteristic activation function, which maps the input color information into different shades of color components, thereby yielding a processed component color image segmented on the basis of the different shades of component colors. The number of target classes in the segmented image corresponds to the number of levels in the multilevel activation function. Since the multilevel version of the activation function exhibits several subnormal responses to the input color image scene information, the system errors of the three component network architectures are computed from some subnormal linear index of fuzziness of the component color image scenes at the individual level. Several multilevel activation functions are employed for segmentation of the input color image scene using the proposed network architecture. Results of the application of the multilevel activation functions to the PSONN architecture are reported on three real life true color images. The results are substantiated empirically with the correlation coefficients between the segmented images and the original images.
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Academic Press, 1984.
[2] R. C. Gonzalez and P. Wintz, Digital Image Processing, MA: Addison-
Wesley, 1977.
[3] A. Mark Ruzon and Carlo Tomasi, "Edge, junction, and corner detection
using color distributions," IEEE Transactions On Pattern Analysis And
Machine Intelligence, vol. 23, no. 11, pp. 1281-1295, 2001.
[4] N. Vandenbroucke, L. Macaire, and J.-G. Postaire, "Color image segmentation
by pixel classifi cation in an adapted hybrid color space. Application
to soccer image analysis," Computer Vision and Image Understanding,
vol. 90, no. 2, pp. 190-216, 2003.
[5] O. Lezoray and H. Cardot, "Cooperation of color pixel classifi cation
schemes and color watershed : a study for microscopical images," IEEE
Transactions on Image Processing, vol. 11, no. 7, pp. 783-789, 2002.
[6] C. Meurie, G. Lebrun, O. Lezoray, H. Cardot, and A. Elmoataz, "A
comparison of supervised pixel based color image segmentation methods,"
WSEAS Transactions on Computers, vol. 2, no. 3, pp. 739-744,
2003.
[7] S. Belongie, C. Carson, H. Greenspan and J. Malik, "Color- and texturebased
image segmentation using EM and its application to content-based
image retrieval," Proceedings of International Conference on Computer
Vision, pp. 675-682, 1998.
[8] Y. Delignon, A. Marzouli and W. Pieczynski, "Estimation of generalized
mixtures and its application in image segmentation," IEEE Transactions
on Image Processing, vol. 6, no. 10, pp. 1364-1376, 1997.
[9] D. A. Langan, J. W. Modestino and J. Zhang, "Cluster validation for
unsupervised stochastic model-based image segmentation," IEEE Transactions
on Image Processing, vol. 7, no. 2, pp. 180-244, 1997.
[10] D. K. Panjwani and G. Healey, "Markov random fi eld models for
unsupervised segmentation of textured color images," IEEE Transactions
on Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. 939-
954, 1995.
[11] J. -P. Wang, "Stochastic relaxation on partitions with connected components
and it application to image segmentation," IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 20, no. 6, pp. 619-636,
1998.
[12] L. Shafarenko, M. Petrou and J. Kittler, "Automatic watershed segmen-
tation of randomly textured color images," IEEE Transactions on Image
Processing, vol. 6, no. 11, pp. 1530-1544, 1997.
[13] W. Y. Ma and B. S. Manjunath, "Edge flow: a framework for boundary
detection and image segmentation," Proceedings of IEEE Conference on
Computer Vision and Pattern Recognition, pp. 744-749, 1997.
[14] J. Shi and J. Malik, "Normalized cuts and image segmentation," Proceedings
of IEEE 6th International Conference on Computer Vision, pp.
1154-1160, 1998.
[15] H.D. Cheng, X.H. Jiang, Y. Sun, and J. Xang, "Color image segmentation:
advances and prospects," Pattern Recognition, vol. 34, pp. 2259-
2281, 2001.
[16] F. Ennesser and G. Medioni, "Finding waldo, or focus of attention
using local color information," IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 17, no. 8, 1995.
[17] J. R. Smith and S. -F. Chang, "Single color extraction and image
query," IEEE International Conference on Image Processing, 1995.
[18] W. Hsu, T. S. Chua and H. K. Pung, "An integrated color-spatial
approach to content -based image retrieval," ACM Multimedia 1995, 1995.
[19] Arnold W. M. Smeulders, Marcel Worring, Simone Santini, Amarnath
Gupta and Ramesh Jain, "Content-Based Image Retrieval at the End of
the Early Years," IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 22, no. 12, 2000.
[20] Sung-Hyuk Cha, "A Fast Hue-Based Color Image Indexing Algorithm,"
Machine Graphics and Vision, vol. 11, no. 2/3, pp. 285-295, 2002.
[21] Sung-Hyuk Cha and Srikanth Munirathnam, "Comparing Color Images
using Angular Histogram Measures," Proceedings of 5th JCIS, vol. 2, pp.
139-142, 2000.
[22] G. Pass, R. Zabih and J. Miller, "Comparing Images using Color
Coherence," Proceedings of ACM International Multimedia Conference,
pp. 65-73, 1998.
[23] H. S. Sawhney and J. L. Hafner, "Effi cient Color Histogram Indexing,"
Proceedings of International Conference on Image Processing, vol.
1, pp. 66-70, 1994.
[24] J. L. Hafner, H. S. Sawhney, W. Equitz, M. Flickner and W. Niblack,
"Effi cient Color Histogram Indexing for quadratic form distance functions,"
IEEE Transactions on Pattern Analysis and Machine Intelligence,
1995.
[25] M. J. Swain and D. H. Ballard, "Color Indexing," International Journal
of Computer Vision, vol. 7, pp. 11-32, 1991.
[26] Lee Jung-Hum and Jo Kang-Hyum, "Traffi c Sign Recognition by
Division of Characters and Symbols Regions," Proceedings of 7th Korea-
Russia International Symposium, KORUS 2003, pp. 324-328.
[27] Y. Chen, H. Hwang and B. Chen, "Color Image Analysis Using
Fuzzy Set Theory," Proceedings of International Conference on Image
Processing, pp. 242-245, 1995.
[28] Y. Choi and R. Krishnapuran, "Image Enhancement Based on Fuzzy
Logic," Proceedings of International Conference on Image Processing,
pp. 167-170, 1995.
[29] F. Chung and B. Fung, "Fuzzy Color Quantization and its Application
to Scene Change Detection," MIR-03, pp. 157-162, Berkeley, California,
USA, 2003.
[30] N. Ito, Y. Shimazu, T. Yokoyama and Y. Matushita, "Fuzzy Logic
Based Non-Parametric Color Image Segmentation with Optional Block
Processing," ACM, pp. 119-126, 0-89791-737-5, 1995.
[31] A. Gillet, L. Macaire, C. Botte Lecocq, and J. G. Postaire, "Color image
segmentation by analysis of 3d histogram with fuzzy morphological
fi lters," Studies in Fuzziness and Soft Computing, vol. 122, pp. 153-177,
2002.
[32] Eduardo Hart, Sung-Hyuk Cha and Charles Tappert, "Interactive Flag
Identifi cation Using a Fuzzy-Neural Technique," Proceedings of Student/
Faculty Research Day, CSIS, 2004.
[33] A. Ghosh, N. R. Pal and S. K. Pal, "Self-organization for object extraction
using a multilayer neural network and fuzziness measures," IEEE
Transactions on Fuzzy Systems, vol. 1, no.1, pp. 54-68, 1993.
[34] S. Bhattacharyya and K. Dasgupta, "Color Object Extraction From A
Noisy Background Using Parallel Multi-layer Self-Organizing Neural
Networks," Proceedings of CSI-YITPA(E) 2003, pp. 32-36, 2003.
[35] K. Dasgupta, S. Bhattacharyya and P. Dutta, "Pure Color Object Extraction:
A Distributed Approach," Proceedings of ObComAPC 2004, 2004.
[36] S. Bhattacharyya, P. Dutta and U. Maulik, "Multi-Scale Object Extraction
Using Self Organizing Neural Network With A Multi-Level Sigmoidal
Activation Function," Proceedings of 5th International Conference
on Advances in Pattern Recognition, pp. 435-438, 2003.
[37] P. Dutta, S. Bhattacharyya and K. Dasgupta, "Multi-Scale Object Extraction
Using A Self Organizing Neural Network With A Multi-Level
Beta Activation Function," Proceedings of International Conference on
Intelligent Sensing and Information Processing (ICISIP 2004), pp. 139-
142, 2004.
[38] S. Bhattacharyya, P. Dutta and D. DuttaMajumder, "Multiscale Object
Extraction Using A Self-Organizing Neural Network With Multilevel
Beta Activation Function and its Sigmoidal Counterpart: A Comparative
Study," Proceedings of International Conference on Recent Trends and
New Directions of Research in Cybernetics & Systems Theory, 2004.
[39] S. Bhattacharyya and P. Dutta, "Multiscale Object Extraction with
MUSIG and MUBET with CONSENT: A Comparative Study," Proceedings
of KBCS 2004, pp. 100-109, 2004.
[40] S. Bhattacharyya and P. Dutta, "XMUBET with CONSENT: A Pixel
Hostility Induced Multiscale Object Extractor," Proceedings of IEEE
International Conference on Intelligent Sensors, Sensor Networks and
Information Processing (ISSNIP04), pp. 277-282, 2004.
[41] S. Bhattacharyya and P. Dutta, "XMUSIG with CONSENT: Pixel Hostility
Induced Multiscale Object Extractor," Proceedings of International
Conference on Intelligent Sensing and Information Processing (ICISIP
2005), 2005.
[42] S. Bhattacharyya and P. Dutta, "Designing pruned neighborhood neural
networks for object extraction from noisy background," Journal of Foundations
of Computing and Decision Sciences, vol 31, no. 2, pp. 105-134,
2006.
[43] A. Kandel, Fuzzy Mathematical Techniques with Applications, New
York: Addison-Wesley, 1986.
[44] L. A. Zadeh, "Fuzzy Sets," Inform. and Control, vol. 8, pp. 338-353,
1965.
[45] T. J. Ross and T. Ross, Fuzzy Logic With Engineering Applications,
McGraw Hill College Div., 1995.
[1] M. P. Ekstrom, eds. Digital Image Processing Techniques, New York:
Academic Press, 1984.
[2] R. C. Gonzalez and P. Wintz, Digital Image Processing, MA: Addison-
Wesley, 1977.
[3] A. Mark Ruzon and Carlo Tomasi, "Edge, junction, and corner detection
using color distributions," IEEE Transactions On Pattern Analysis And
Machine Intelligence, vol. 23, no. 11, pp. 1281-1295, 2001.
[4] N. Vandenbroucke, L. Macaire, and J.-G. Postaire, "Color image segmentation
by pixel classifi cation in an adapted hybrid color space. Application
to soccer image analysis," Computer Vision and Image Understanding,
vol. 90, no. 2, pp. 190-216, 2003.
[5] O. Lezoray and H. Cardot, "Cooperation of color pixel classifi cation
schemes and color watershed : a study for microscopical images," IEEE
Transactions on Image Processing, vol. 11, no. 7, pp. 783-789, 2002.
[6] C. Meurie, G. Lebrun, O. Lezoray, H. Cardot, and A. Elmoataz, "A
comparison of supervised pixel based color image segmentation methods,"
WSEAS Transactions on Computers, vol. 2, no. 3, pp. 739-744,
2003.
[7] S. Belongie, C. Carson, H. Greenspan and J. Malik, "Color- and texturebased
image segmentation using EM and its application to content-based
image retrieval," Proceedings of International Conference on Computer
Vision, pp. 675-682, 1998.
[8] Y. Delignon, A. Marzouli and W. Pieczynski, "Estimation of generalized
mixtures and its application in image segmentation," IEEE Transactions
on Image Processing, vol. 6, no. 10, pp. 1364-1376, 1997.
[9] D. A. Langan, J. W. Modestino and J. Zhang, "Cluster validation for
unsupervised stochastic model-based image segmentation," IEEE Transactions
on Image Processing, vol. 7, no. 2, pp. 180-244, 1997.
[10] D. K. Panjwani and G. Healey, "Markov random fi eld models for
unsupervised segmentation of textured color images," IEEE Transactions
on Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. 939-
954, 1995.
[11] J. -P. Wang, "Stochastic relaxation on partitions with connected components
and it application to image segmentation," IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 20, no. 6, pp. 619-636,
1998.
[12] L. Shafarenko, M. Petrou and J. Kittler, "Automatic watershed segmen-
tation of randomly textured color images," IEEE Transactions on Image
Processing, vol. 6, no. 11, pp. 1530-1544, 1997.
[13] W. Y. Ma and B. S. Manjunath, "Edge flow: a framework for boundary
detection and image segmentation," Proceedings of IEEE Conference on
Computer Vision and Pattern Recognition, pp. 744-749, 1997.
[14] J. Shi and J. Malik, "Normalized cuts and image segmentation," Proceedings
of IEEE 6th International Conference on Computer Vision, pp.
1154-1160, 1998.
[15] H.D. Cheng, X.H. Jiang, Y. Sun, and J. Xang, "Color image segmentation:
advances and prospects," Pattern Recognition, vol. 34, pp. 2259-
2281, 2001.
[16] F. Ennesser and G. Medioni, "Finding waldo, or focus of attention
using local color information," IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 17, no. 8, 1995.
[17] J. R. Smith and S. -F. Chang, "Single color extraction and image
query," IEEE International Conference on Image Processing, 1995.
[18] W. Hsu, T. S. Chua and H. K. Pung, "An integrated color-spatial
approach to content -based image retrieval," ACM Multimedia 1995, 1995.
[19] Arnold W. M. Smeulders, Marcel Worring, Simone Santini, Amarnath
Gupta and Ramesh Jain, "Content-Based Image Retrieval at the End of
the Early Years," IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 22, no. 12, 2000.
[20] Sung-Hyuk Cha, "A Fast Hue-Based Color Image Indexing Algorithm,"
Machine Graphics and Vision, vol. 11, no. 2/3, pp. 285-295, 2002.
[21] Sung-Hyuk Cha and Srikanth Munirathnam, "Comparing Color Images
using Angular Histogram Measures," Proceedings of 5th JCIS, vol. 2, pp.
139-142, 2000.
[22] G. Pass, R. Zabih and J. Miller, "Comparing Images using Color
Coherence," Proceedings of ACM International Multimedia Conference,
pp. 65-73, 1998.
[23] H. S. Sawhney and J. L. Hafner, "Effi cient Color Histogram Indexing,"
Proceedings of International Conference on Image Processing, vol.
1, pp. 66-70, 1994.
[24] J. L. Hafner, H. S. Sawhney, W. Equitz, M. Flickner and W. Niblack,
"Effi cient Color Histogram Indexing for quadratic form distance functions,"
IEEE Transactions on Pattern Analysis and Machine Intelligence,
1995.
[25] M. J. Swain and D. H. Ballard, "Color Indexing," International Journal
of Computer Vision, vol. 7, pp. 11-32, 1991.
[26] Lee Jung-Hum and Jo Kang-Hyum, "Traffi c Sign Recognition by
Division of Characters and Symbols Regions," Proceedings of 7th Korea-
Russia International Symposium, KORUS 2003, pp. 324-328.
[27] Y. Chen, H. Hwang and B. Chen, "Color Image Analysis Using
Fuzzy Set Theory," Proceedings of International Conference on Image
Processing, pp. 242-245, 1995.
[28] Y. Choi and R. Krishnapuran, "Image Enhancement Based on Fuzzy
Logic," Proceedings of International Conference on Image Processing,
pp. 167-170, 1995.
[29] F. Chung and B. Fung, "Fuzzy Color Quantization and its Application
to Scene Change Detection," MIR-03, pp. 157-162, Berkeley, California,
USA, 2003.
[30] N. Ito, Y. Shimazu, T. Yokoyama and Y. Matushita, "Fuzzy Logic
Based Non-Parametric Color Image Segmentation with Optional Block
Processing," ACM, pp. 119-126, 0-89791-737-5, 1995.
[31] A. Gillet, L. Macaire, C. Botte Lecocq, and J. G. Postaire, "Color image
segmentation by analysis of 3d histogram with fuzzy morphological
fi lters," Studies in Fuzziness and Soft Computing, vol. 122, pp. 153-177,
2002.
[32] Eduardo Hart, Sung-Hyuk Cha and Charles Tappert, "Interactive Flag
Identifi cation Using a Fuzzy-Neural Technique," Proceedings of Student/
Faculty Research Day, CSIS, 2004.
[33] A. Ghosh, N. R. Pal and S. K. Pal, "Self-organization for object extraction
using a multilayer neural network and fuzziness measures," IEEE
Transactions on Fuzzy Systems, vol. 1, no.1, pp. 54-68, 1993.
[34] S. Bhattacharyya and K. Dasgupta, "Color Object Extraction From A
Noisy Background Using Parallel Multi-layer Self-Organizing Neural
Networks," Proceedings of CSI-YITPA(E) 2003, pp. 32-36, 2003.
[35] K. Dasgupta, S. Bhattacharyya and P. Dutta, "Pure Color Object Extraction:
A Distributed Approach," Proceedings of ObComAPC 2004, 2004.
[36] S. Bhattacharyya, P. Dutta and U. Maulik, "Multi-Scale Object Extraction
Using Self Organizing Neural Network With A Multi-Level Sigmoidal
Activation Function," Proceedings of 5th International Conference
on Advances in Pattern Recognition, pp. 435-438, 2003.
[37] P. Dutta, S. Bhattacharyya and K. Dasgupta, "Multi-Scale Object Extraction
Using A Self Organizing Neural Network With A Multi-Level
Beta Activation Function," Proceedings of International Conference on
Intelligent Sensing and Information Processing (ICISIP 2004), pp. 139-
142, 2004.
[38] S. Bhattacharyya, P. Dutta and D. DuttaMajumder, "Multiscale Object
Extraction Using A Self-Organizing Neural Network With Multilevel
Beta Activation Function and its Sigmoidal Counterpart: A Comparative
Study," Proceedings of International Conference on Recent Trends and
New Directions of Research in Cybernetics & Systems Theory, 2004.
[39] S. Bhattacharyya and P. Dutta, "Multiscale Object Extraction with
MUSIG and MUBET with CONSENT: A Comparative Study," Proceedings
of KBCS 2004, pp. 100-109, 2004.
[40] S. Bhattacharyya and P. Dutta, "XMUBET with CONSENT: A Pixel
Hostility Induced Multiscale Object Extractor," Proceedings of IEEE
International Conference on Intelligent Sensors, Sensor Networks and
Information Processing (ISSNIP04), pp. 277-282, 2004.
[41] S. Bhattacharyya and P. Dutta, "XMUSIG with CONSENT: Pixel Hostility
Induced Multiscale Object Extractor," Proceedings of International
Conference on Intelligent Sensing and Information Processing (ICISIP
2005), 2005.
[42] S. Bhattacharyya and P. Dutta, "Designing pruned neighborhood neural
networks for object extraction from noisy background," Journal of Foundations
of Computing and Decision Sciences, vol 31, no. 2, pp. 105-134,
2006.
[43] A. Kandel, Fuzzy Mathematical Techniques with Applications, New
York: Addison-Wesley, 1986.
[44] L. A. Zadeh, "Fuzzy Sets," Inform. and Control, vol. 8, pp. 338-353,
1965.
[45] T. J. Ross and T. Ross, Fuzzy Logic With Engineering Applications,
McGraw Hill College Div., 1995.
@article{"International Journal of Information, Control and Computer Sciences:61763", author = "Siddhartha Bhattacharyya and Paramartha Dutta and Ujjwal Maulik and Prashanta Kumar Nandi", title = "Multilevel Activation Functions For True Color Image Segmentation Using a Self Supervised Parallel Self Organizing Neural Network (PSONN) Architecture: A Comparative Study", abstract = "The paper describes a self supervised parallel self organizing neural network (PSONN) architecture for true color image segmentation. The proposed architecture is a parallel extension of the standard single self organizing neural network architecture (SONN) and comprises an input (source) layer of image information, three single self organizing neural network architectures for segmentation of the different primary color components in a color image scene and one final output (sink) layer for fusion of the segmented color component images. Responses to the different shades of color components are induced in each of the three single network architectures (meant for component level processing) by applying a multilevel version of the characteristic activation function, which maps the input color information into different shades of color components, thereby yielding a processed component color image segmented on the basis of the different shades of component colors. The number of target classes in the segmented image corresponds to the number of levels in the multilevel activation function. Since the multilevel version of the activation function exhibits several subnormal responses to the input color image scene information, the system errors of the three component network architectures are computed from some subnormal linear index of fuzziness of the component color image scenes at the individual level. Several multilevel activation functions are employed for segmentation of the input color image scene using the proposed network architecture. Results of the application of the multilevel activation functions to the PSONN architecture are reported on three real life true color images. The results are substantiated empirically with the correlation coefficients between the segmented images and the original images.
", keywords = "Colour image segmentation, fuzzy set theory, multi-level activation functions, parallel self-organizing neural network.", volume = "1", number = "8", pages = "2585-13", }
