Color image segmentation plays an important role in
computer vision and image processing areas. In this paper, the
features of Volterra filter are utilized for color image segmentation.
The discrete Volterra filter exhibits both linear and nonlinear
characteristics. The linear part smoothes the image features in
uniform gray zones and is used for getting a gross representation of
objects of interest. The nonlinear term compensates for the blurring
due to the linear term and preserves the edges which are mainly used
to distinguish the various objects. The truncated quadratic Volterra
filters are mainly used for edge preserving along with Gaussian noise
cancellation. In our approach, the segmentation is based on K-means
clustering algorithm in HSI space. Both the hue and the intensity
components are fully utilized. For hue clustering, the special cyclic
property of the hue component is taken into consideration. The
experimental results show that the proposed technique segments the
color image while preserving significant features and removing noise
effects.
[1] Y. Rui, T.S Huang and S.F.Chang, "Image retrieval: Current techniques,
promising directions and open issues", Journal of Visual
Communication and Image Representation, vol. 10, 1999, 39-62.
[2] W.M.Smeulders, M. Worring, S.Santini, A.Gupta and R.Jain, "Contentbased
image retrieval at the end of early years", IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol.22, 2000, 1349-1379.
[3] T.Randen and J.H.Husoy, "Texture segmentation using filters with
optimized energy separation", IEEE Transactions on IP, vol.8, 1999,
571-582
[4] D. Comanicui and P.Meer, "Mean Shift: A robust approach towards
feature space analysis", IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol.24, N0:5, 2005.
[5] Simon Haykin, Neural Networks A Comprehensive Foundation (NJ:
Pearson Education, 1999).
[6] Jacek M. Zurada, Introduction to Artificial Neural Systems (NJ : Jaico
publishers, 2002)
[7] L.O.Hall, A. Bensaid, L.Clarke, R.Velthuizen, M.Silbiger, J. Bezdek,
"A comparison of neural network and fuzzy clustering techniques in
segmenting magnetic resonance images of the brain", IEEE
Transactions on Neural Networks, vol.3, 1992, 672-682.
[8] S.K.Pal, "Image segmentation using fuzzy correlation, Information
Science, 62, 1992, 223-250
[9] Y. Zhang, "A survey on evaluation methods for image segmentation",
Pattern Recognition, 29(8), 1996, 1335-1346.
[10] H.D. Cheng, X.H. Jiang, Y. Sun, Jingli Wang, "Color image
segmentation: advances and prospectus", Pattern Recognition, 34, 2001,
2259-2281.
[11] V. Boskovitz, Hugo Guteman, "An adaptive neuro fuzzy system for
automatic image segmentation and edge detection", IEEE Transactions
on fuzzy Systems, 10(2), 2002, 247-262.
[12] J. Canny, "A computational approach to edge detection", IEEE
Transactions on Pattern Analysis and Machine Intelligence, 8(6), 1986,
679-698.
[13] Milan Sonka, Vaclav Hlavac, Roger Boyle, Image Processing,
Analysis, and Machine Vision (NJ : Brooks / Cole publishers).
[14] R. Adams, L. Bischof, "Seeded region growing", IEEE Transactions on
Pattern Analysis and Machine Intelligence, 16, 1994, 641- 647.
[15] T. Q. Chen, Y. Lu, "Color image segmentation-an innovative approach",
Pattern Recognition, 25, 2001, 395-405.
[16] Xu Jie, Shi Peng Fei, "Natural color image segmentation", International
Conference on Image Processing, 2003, 973-976.
[17] Ety Navon, Often Miller, Amir Averabuch, "Color image segmentation
based on adaptive local thresholds", Image and Vision Computing, 23,
2005, 69-85.
[18] S. Ji, H.W. Park, "Image segmentation of color image based on region
coherency", International Conference on Image Processing, 1998, 80-
83.
[19] You Shen Lo, Soo Chang Pei, "Color image segmentation using local
histogram and self organization of Kohonen feature map", International
Conference on Image Processing, 1999, 232-239.
[20] N Li, Y.F. Li, "Feature encoding for unsupervised segmentation of
color images", IEEE Transactions System Man Cybernetics (SMC),
33(3), 2003, 438-446.
[21] S.K. Pal, A. Rosenfield, "Image enhancement and thresholding by
optimization of fuzzy compactness", Pattern Recognition Letter, 7,
1988, 77-86.
[22] Sing-Tze Bow, Pattern Recognition (NJ : Marcel Dekker, 1984).
[23] Thierry Carron, Patrick Lambert, "Color edge detector using jointly hue,
saturation and intensity", International Conference on Image
Processing, 1994, 977-981.
[24] M .B. Meenavathi, K. Rajesh, "Volterra Filtering techniques for
removal of Gaussian and mixed Gaussian-Impulse noise"
InternationalJournal of applied mathematics and computer science, vol
4(9), 2007, 51- 56.
[1] Y. Rui, T.S Huang and S.F.Chang, "Image retrieval: Current techniques,
promising directions and open issues", Journal of Visual
Communication and Image Representation, vol. 10, 1999, 39-62.
[2] W.M.Smeulders, M. Worring, S.Santini, A.Gupta and R.Jain, "Contentbased
image retrieval at the end of early years", IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol.22, 2000, 1349-1379.
[3] T.Randen and J.H.Husoy, "Texture segmentation using filters with
optimized energy separation", IEEE Transactions on IP, vol.8, 1999,
571-582
[4] D. Comanicui and P.Meer, "Mean Shift: A robust approach towards
feature space analysis", IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol.24, N0:5, 2005.
[5] Simon Haykin, Neural Networks A Comprehensive Foundation (NJ:
Pearson Education, 1999).
[6] Jacek M. Zurada, Introduction to Artificial Neural Systems (NJ : Jaico
publishers, 2002)
[7] L.O.Hall, A. Bensaid, L.Clarke, R.Velthuizen, M.Silbiger, J. Bezdek,
"A comparison of neural network and fuzzy clustering techniques in
segmenting magnetic resonance images of the brain", IEEE
Transactions on Neural Networks, vol.3, 1992, 672-682.
[8] S.K.Pal, "Image segmentation using fuzzy correlation, Information
Science, 62, 1992, 223-250
[9] Y. Zhang, "A survey on evaluation methods for image segmentation",
Pattern Recognition, 29(8), 1996, 1335-1346.
[10] H.D. Cheng, X.H. Jiang, Y. Sun, Jingli Wang, "Color image
segmentation: advances and prospectus", Pattern Recognition, 34, 2001,
2259-2281.
[11] V. Boskovitz, Hugo Guteman, "An adaptive neuro fuzzy system for
automatic image segmentation and edge detection", IEEE Transactions
on fuzzy Systems, 10(2), 2002, 247-262.
[12] J. Canny, "A computational approach to edge detection", IEEE
Transactions on Pattern Analysis and Machine Intelligence, 8(6), 1986,
679-698.
[13] Milan Sonka, Vaclav Hlavac, Roger Boyle, Image Processing,
Analysis, and Machine Vision (NJ : Brooks / Cole publishers).
[14] R. Adams, L. Bischof, "Seeded region growing", IEEE Transactions on
Pattern Analysis and Machine Intelligence, 16, 1994, 641- 647.
[15] T. Q. Chen, Y. Lu, "Color image segmentation-an innovative approach",
Pattern Recognition, 25, 2001, 395-405.
[16] Xu Jie, Shi Peng Fei, "Natural color image segmentation", International
Conference on Image Processing, 2003, 973-976.
[17] Ety Navon, Often Miller, Amir Averabuch, "Color image segmentation
based on adaptive local thresholds", Image and Vision Computing, 23,
2005, 69-85.
[18] S. Ji, H.W. Park, "Image segmentation of color image based on region
coherency", International Conference on Image Processing, 1998, 80-
83.
[19] You Shen Lo, Soo Chang Pei, "Color image segmentation using local
histogram and self organization of Kohonen feature map", International
Conference on Image Processing, 1999, 232-239.
[20] N Li, Y.F. Li, "Feature encoding for unsupervised segmentation of
color images", IEEE Transactions System Man Cybernetics (SMC),
33(3), 2003, 438-446.
[21] S.K. Pal, A. Rosenfield, "Image enhancement and thresholding by
optimization of fuzzy compactness", Pattern Recognition Letter, 7,
1988, 77-86.
[22] Sing-Tze Bow, Pattern Recognition (NJ : Marcel Dekker, 1984).
[23] Thierry Carron, Patrick Lambert, "Color edge detector using jointly hue,
saturation and intensity", International Conference on Image
Processing, 1994, 977-981.
[24] M .B. Meenavathi, K. Rajesh, "Volterra Filtering techniques for
removal of Gaussian and mixed Gaussian-Impulse noise"
InternationalJournal of applied mathematics and computer science, vol
4(9), 2007, 51- 56.
@article{"International Journal of Information, Control and Computer Sciences:60893", author = "M. B. Meenavathi and K. Rajesh", title = "Volterra Filter for Color Image Segmentation", abstract = "Color image segmentation plays an important role in
computer vision and image processing areas. In this paper, the
features of Volterra filter are utilized for color image segmentation.
The discrete Volterra filter exhibits both linear and nonlinear
characteristics. The linear part smoothes the image features in
uniform gray zones and is used for getting a gross representation of
objects of interest. The nonlinear term compensates for the blurring
due to the linear term and preserves the edges which are mainly used
to distinguish the various objects. The truncated quadratic Volterra
filters are mainly used for edge preserving along with Gaussian noise
cancellation. In our approach, the segmentation is based on K-means
clustering algorithm in HSI space. Both the hue and the intensity
components are fully utilized. For hue clustering, the special cyclic
property of the hue component is taken into consideration. The
experimental results show that the proposed technique segments the
color image while preserving significant features and removing noise
effects.", keywords = "Color image segmentation, HSI space, K–means
clustering, Volterra filter.", volume = "1", number = "11", pages = "3620-6", }