Color Image Segmentation Using Competitive and Cooperative Learning Approach
Color image segmentation can be considered as a
cluster procedure in feature space. k-means and its adaptive
version, i.e. competitive learning approach are powerful tools
for data clustering. But k-means and competitive learning suffer
from several drawbacks such as dead-unit problem and need to
pre-specify number of cluster. In this paper, we will explore to
use competitive and cooperative learning approach to perform
color image segmentation. In competitive and cooperative
learning approach, seed points not only compete each other, but
also the winner will dynamically select several nearest
competitors to form a cooperative team to adapt to the input
together, finally it can automatically select the correct number
of cluster and avoid the dead-units problem. Experimental
results show that CCL can obtain better segmentation result.
[1] N. R. Pal, S.K. Pal, "A review on image segmentation techniques,"
Pattern Recognition, vol. 26, pp. 1277-1291, Sep. 1993.
[2] H. D. Chen, X. H. Jiang, Y. Sun, J.L. Wang, "Color image segmentation:
advances and prospects," Pattern Recognition, vol. 34, pp. 2259-2281,
2001.
[3] T. Uchiyama, M. A. Arbib, "Color image segmentation using competitive
learning," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.
12, Dec. 1994, pp.1197-1206.
[4] D. Comanniciu, E. Meer, "Robust analysis of feature space: color image
segmentation," in In Proc. IEEE Conf. on Computer Vision and Pattern
Recognition, Puerto Rico, 1997, pp. 750-755.
[5] H. Palus, M. Bogdanski, "Clustering techniques in color image
segmentation," in Proc. of Methods of Artificial Intelligence, Gliwice,
Poland, 2003, pp. 103-104.
[6] D. E. Rumelhart, D. Zipser, "Feature discovery by competitive learning,"
Cognitive Science, vol. 9, pp. 75-112, 1985.
[7] L. Xu, A. Krzyzak, E. Oja, "Rival penalized competitive learning for
clustering analysis, RBF net, and curve detection," IEEE Trans. Neural
Network, vol. 4, pp.636-649, July, 1993.
[8] S. C. Ahalt, A. K. Krishnamurthy, P. Chen, D. E. Melton, "Competitive
learning algorithms for vector quantization," Neural Networks, vol. 3, pp.
277-291, 1990.
[9] Y. M. Cheung, "A competitive and cooperative learning approach to
robust data clustering," Dept. of Computer Science, Hong Kong Baptist
University, Technical Report: COMP-03-021, 2003.
[10] Y. Ohta, T. Kanade, T. Sakai, "Color information for region
segmentation," Computer Graphics Image Process. vol. 13, pp. 222-241,
1980.
[1] N. R. Pal, S.K. Pal, "A review on image segmentation techniques,"
Pattern Recognition, vol. 26, pp. 1277-1291, Sep. 1993.
[2] H. D. Chen, X. H. Jiang, Y. Sun, J.L. Wang, "Color image segmentation:
advances and prospects," Pattern Recognition, vol. 34, pp. 2259-2281,
2001.
[3] T. Uchiyama, M. A. Arbib, "Color image segmentation using competitive
learning," IEEE Trans. Pattern Analysis and Machine Intelligence, vol.
12, Dec. 1994, pp.1197-1206.
[4] D. Comanniciu, E. Meer, "Robust analysis of feature space: color image
segmentation," in In Proc. IEEE Conf. on Computer Vision and Pattern
Recognition, Puerto Rico, 1997, pp. 750-755.
[5] H. Palus, M. Bogdanski, "Clustering techniques in color image
segmentation," in Proc. of Methods of Artificial Intelligence, Gliwice,
Poland, 2003, pp. 103-104.
[6] D. E. Rumelhart, D. Zipser, "Feature discovery by competitive learning,"
Cognitive Science, vol. 9, pp. 75-112, 1985.
[7] L. Xu, A. Krzyzak, E. Oja, "Rival penalized competitive learning for
clustering analysis, RBF net, and curve detection," IEEE Trans. Neural
Network, vol. 4, pp.636-649, July, 1993.
[8] S. C. Ahalt, A. K. Krishnamurthy, P. Chen, D. E. Melton, "Competitive
learning algorithms for vector quantization," Neural Networks, vol. 3, pp.
277-291, 1990.
[9] Y. M. Cheung, "A competitive and cooperative learning approach to
robust data clustering," Dept. of Computer Science, Hong Kong Baptist
University, Technical Report: COMP-03-021, 2003.
[10] Y. Ohta, T. Kanade, T. Sakai, "Color information for region
segmentation," Computer Graphics Image Process. vol. 13, pp. 222-241,
1980.
@article{"International Journal of Information, Control and Computer Sciences:61191", author = "Yinggan Tang and Xinping Guan", title = "Color Image Segmentation Using Competitive and Cooperative Learning Approach", abstract = "Color image segmentation can be considered as a
cluster procedure in feature space. k-means and its adaptive
version, i.e. competitive learning approach are powerful tools
for data clustering. But k-means and competitive learning suffer
from several drawbacks such as dead-unit problem and need to
pre-specify number of cluster. In this paper, we will explore to
use competitive and cooperative learning approach to perform
color image segmentation. In competitive and cooperative
learning approach, seed points not only compete each other, but
also the winner will dynamically select several nearest
competitors to form a cooperative team to adapt to the input
together, finally it can automatically select the correct number
of cluster and avoid the dead-units problem. Experimental
results show that CCL can obtain better segmentation result.", keywords = "Color image segmentation, competitive learning,cluster, k-means algorithm, competitive and cooperative learning.", volume = "2", number = "1", pages = "184-4", }