Color Image Segmentation and Multi-Level Thresholding by Maximization of Conditional Entropy
In this work a novel approach for color image
segmentation using higher order entropy as a textural feature for
determination of thresholds over a two dimensional image histogram
is discussed. A similar approach is applied to achieve multi-level
thresholding in both grayscale and color images. The paper discusses
two methods of color image segmentation using RGB space as the
standard processing space. The threshold for segmentation is decided
by the maximization of conditional entropy in the two dimensional
histogram of the color image separated into three grayscale images of
R, G and B. The features are first developed independently for the
three ( R, G, B ) spaces, and combined to get different color
component segmentation. By considering local maxima instead of the
maximum of conditional entropy yields multiple thresholds for the
same image which forms the basis for multilevel thresholding.
[1] Yo-Ping Huang and Tsun-Wei Chang , "Fuzzy Inference Model for
Image Segmentation", 2003 IEEE Int. Conf. on Fuzzy Systems, pp.972-
977,2003W.-K. Chen, Linear Networks and Systems (Book style).
Belmont, CA: Wadsworth, 1993, pp. 123-135.
[2] Mario Costantani, Massimo Zavagli, and Giovanni Millillo, "A Novel
Approach for Image Segmentation",2002 IEEE tran. on Geoscience and
Remote Sensing, pp.1603-1605,2002..
[3] Gour C. Karmakar and Laurence S. Dooley, "Extended Fuzzy Rules for
Image Segmentation",2001 IEEE..
[4] A. D. Brink, "Thresholding of Digital Images Using two dimentional.
Entropies", 1992 Pattern Recognition, vol. 25,no. 8.pp.803-808,1992
Pattern Recognition Society.J. Wang, "Fundamentals of erbium-doped
fiber amplifiers arrays (Periodical styleÔÇöSubmitted for publication),"
IEEE J. Quantum Electron., submitted for publication.
[5] Christopher Tresp, Markaus Jager, Michael Moser, Jens Hiltner , Madjid
Fathi ,"A New Method for Image Segmentation based on Fuzzy
Knowledge", 1996 IEEE.
[6] Z. Chi and H.Yan, "Map Image Segmentation based on Thresholding
and Fuzzy Rules",1993 IEE Electronics letters, Vol. 29, pp. 1841-
1843,1993M. Young, The Technical Writers Handbook. Mill Valley,
CA: University Science, 1989.
[7] N.R.Pal and S.K.Pal, "Object background segmentation using new
definition of entropy", 1989 IEE proceedings, Vol. 136, pp. 284-295,
1989 S. Chen, B. Mulgrew, and P. M. Grant, "A clustering technique
for digital communications channel equalization using radial basis
function networks," IEEE Trans. Neural Networks, vol. 4, pp. 570-578,
July 1993.
[8] Wojciech Bieniecki and Szymon Grabowski, "Nearest Neighbor
Classifiers for Color Image Segmentation", TCSET- 2004 Feb 24-28,
2004, pp. 209-212,Lviv-Slavsko,Ukraine.S. P. Bingulac, "On the
compatibility of adaptive controllers (Published Conference Proceedings
style)," in Proc. 4th Annu. Allerton Conf. Circuits and Systems Theory,
New York, 1994, pp. 8-16.
[9] C. V. Jawahar and A.K.Ray "Techniques and applications of fuzzy
statistics in Digital Image Analysis". IEEE signal processing, 1999, PP-
759-771W. D. Doyle, "Magnetization reversal in films with biaxial
anisotropy," in 1987 Proc. INTERMAG Conf., pp. 2.2-1-2.2-6.
[10] C .V. Jawahar and A.K.Ray. "Fuzzy Statistics of Digital Images", IEEE
Signal Processing Letters, Vol.3,NO.8, August PP:225-227,1995.
[11] Robert M. Haralick, K. Shanmugam, and Dinstein. I. "Textural Features
for Image Classification" IEEE trans. Systems Man Cybernet Vol.3,
pp:610-621,1973.
[12] Ben S. Runnacles and Mark S. Nixon, "Textural Extraction and
Segmentation via Statistical Geometric Features", IEEE tran.pp.129-
132,1996N. Kawasaki, "Parametric study of thermal and chemical non
equilibrium nozzle flow," M.S. thesis, Dept. Electron. Eng., Osaka
Univ., Osaka, Japan, 1993.
[13] Liang S. Ng, Mark S. Nixon, John N. Carter, "Textural Classification
using Combined Feature Sets", IEEE tran.pp.103-108,1998IEEE Criteria
for Class IE Electric Systems (Standards style), IEEE Standard 308,
1969.
[14] S. Wang, C. Lam, "Textural Feature Extraction using Gray Level
Gradient Based Co-occurrence Matrices ", IEEE tran.pp.267-271, 1996.
[15] S.K.Pal, N.R.Pal," Object Extraction from Image using Higher Order
Entropy", IEEE tran.pp.348-350,1988.
[16] C.H.Li and C.K.Lee," Minimum Cross Entropy Thresholding", Pattern
Recognition,vol.26,no.4,pp.617-625,1993. (Handbook style)
Transmission Systems for Communications, 3rd ed., Western Electric
Co., Winston-Salem, NC, 1985, pp. 44-60.
[17] Ahmed S.Abutaleb, "Automatic Thresholding of Gray Level Pictures
Using Two Dimensional Entropy", Computer Vision, Graphics and
Image Processing 47,22-32(1989).
[18] N.R.Pal and S.K.Pal, "Entropic Thresholding, "Signal Processing
16(1989) 97-108,North-Holland.
[1] Yo-Ping Huang and Tsun-Wei Chang , "Fuzzy Inference Model for
Image Segmentation", 2003 IEEE Int. Conf. on Fuzzy Systems, pp.972-
977,2003W.-K. Chen, Linear Networks and Systems (Book style).
Belmont, CA: Wadsworth, 1993, pp. 123-135.
[2] Mario Costantani, Massimo Zavagli, and Giovanni Millillo, "A Novel
Approach for Image Segmentation",2002 IEEE tran. on Geoscience and
Remote Sensing, pp.1603-1605,2002..
[3] Gour C. Karmakar and Laurence S. Dooley, "Extended Fuzzy Rules for
Image Segmentation",2001 IEEE..
[4] A. D. Brink, "Thresholding of Digital Images Using two dimentional.
Entropies", 1992 Pattern Recognition, vol. 25,no. 8.pp.803-808,1992
Pattern Recognition Society.J. Wang, "Fundamentals of erbium-doped
fiber amplifiers arrays (Periodical styleÔÇöSubmitted for publication),"
IEEE J. Quantum Electron., submitted for publication.
[5] Christopher Tresp, Markaus Jager, Michael Moser, Jens Hiltner , Madjid
Fathi ,"A New Method for Image Segmentation based on Fuzzy
Knowledge", 1996 IEEE.
[6] Z. Chi and H.Yan, "Map Image Segmentation based on Thresholding
and Fuzzy Rules",1993 IEE Electronics letters, Vol. 29, pp. 1841-
1843,1993M. Young, The Technical Writers Handbook. Mill Valley,
CA: University Science, 1989.
[7] N.R.Pal and S.K.Pal, "Object background segmentation using new
definition of entropy", 1989 IEE proceedings, Vol. 136, pp. 284-295,
1989 S. Chen, B. Mulgrew, and P. M. Grant, "A clustering technique
for digital communications channel equalization using radial basis
function networks," IEEE Trans. Neural Networks, vol. 4, pp. 570-578,
July 1993.
[8] Wojciech Bieniecki and Szymon Grabowski, "Nearest Neighbor
Classifiers for Color Image Segmentation", TCSET- 2004 Feb 24-28,
2004, pp. 209-212,Lviv-Slavsko,Ukraine.S. P. Bingulac, "On the
compatibility of adaptive controllers (Published Conference Proceedings
style)," in Proc. 4th Annu. Allerton Conf. Circuits and Systems Theory,
New York, 1994, pp. 8-16.
[9] C. V. Jawahar and A.K.Ray "Techniques and applications of fuzzy
statistics in Digital Image Analysis". IEEE signal processing, 1999, PP-
759-771W. D. Doyle, "Magnetization reversal in films with biaxial
anisotropy," in 1987 Proc. INTERMAG Conf., pp. 2.2-1-2.2-6.
[10] C .V. Jawahar and A.K.Ray. "Fuzzy Statistics of Digital Images", IEEE
Signal Processing Letters, Vol.3,NO.8, August PP:225-227,1995.
[11] Robert M. Haralick, K. Shanmugam, and Dinstein. I. "Textural Features
for Image Classification" IEEE trans. Systems Man Cybernet Vol.3,
pp:610-621,1973.
[12] Ben S. Runnacles and Mark S. Nixon, "Textural Extraction and
Segmentation via Statistical Geometric Features", IEEE tran.pp.129-
132,1996N. Kawasaki, "Parametric study of thermal and chemical non
equilibrium nozzle flow," M.S. thesis, Dept. Electron. Eng., Osaka
Univ., Osaka, Japan, 1993.
[13] Liang S. Ng, Mark S. Nixon, John N. Carter, "Textural Classification
using Combined Feature Sets", IEEE tran.pp.103-108,1998IEEE Criteria
for Class IE Electric Systems (Standards style), IEEE Standard 308,
1969.
[14] S. Wang, C. Lam, "Textural Feature Extraction using Gray Level
Gradient Based Co-occurrence Matrices ", IEEE tran.pp.267-271, 1996.
[15] S.K.Pal, N.R.Pal," Object Extraction from Image using Higher Order
Entropy", IEEE tran.pp.348-350,1988.
[16] C.H.Li and C.K.Lee," Minimum Cross Entropy Thresholding", Pattern
Recognition,vol.26,no.4,pp.617-625,1993. (Handbook style)
Transmission Systems for Communications, 3rd ed., Western Electric
Co., Winston-Salem, NC, 1985, pp. 44-60.
[17] Ahmed S.Abutaleb, "Automatic Thresholding of Gray Level Pictures
Using Two Dimensional Entropy", Computer Vision, Graphics and
Image Processing 47,22-32(1989).
[18] N.R.Pal and S.K.Pal, "Entropic Thresholding, "Signal Processing
16(1989) 97-108,North-Holland.
@article{"International Journal of Information, Control and Computer Sciences:53676", author = "R.Sukesh Kumar and Abhisek Verma and Jasprit Singh", title = "Color Image Segmentation and Multi-Level Thresholding by Maximization of Conditional Entropy", abstract = "In this work a novel approach for color image
segmentation using higher order entropy as a textural feature for
determination of thresholds over a two dimensional image histogram
is discussed. A similar approach is applied to achieve multi-level
thresholding in both grayscale and color images. The paper discusses
two methods of color image segmentation using RGB space as the
standard processing space. The threshold for segmentation is decided
by the maximization of conditional entropy in the two dimensional
histogram of the color image separated into three grayscale images of
R, G and B. The features are first developed independently for the
three ( R, G, B ) spaces, and combined to get different color
component segmentation. By considering local maxima instead of the
maximum of conditional entropy yields multiple thresholds for the
same image which forms the basis for multilevel thresholding.", keywords = "conditional entropy, multi-level thresholding,
segmentation, two dimensional image histogram", volume = "1", number = "6", pages = "1598-9", }
