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Color Image Segmentation Using Multispectral Random Field Texture Model & Color Content Features
Keywords:
Color Texture, Multispectral Random Field Models, Color Texture SegmentationAbstract
This paper describes a color texture-based image segmentation system. The color texture information is obtained via modeling with the Multispectral Simultaneous Auto Regressive (MSAR) random field model. The general color content characterized by ratios of sample color means is also used. The image is segmented into regions of uniform color texture using an unsupervised histogram clustering approach that utilizes the combination of MSAR and color features. The performance of the system is tested on two databases containing synthetic mosaics of natural textures and natural scenes, respectively
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References
[1] Y. Deng and B. S. Manjunath, Unsupervised Segmentation of Color-Texture Regions in Images and Video, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 23, no. 8, 2001, pp. 800-810.
[2] J. R. Smith and S-F. Chang, Integrated Spatial and Feature Image Query, Multimedia Systems – ACM - © Springer-Verlag 1999, vol. 7, no. 2, 1999, pp. 129-140.
[3] K. B. Eom, Segmentation of monochrome and color textures using moving average modeling approach, Elsevier Science B. V. Image and Vision Computing, no.17, 1999, pp. 233-244.
[4] M. Mirmehdi and M. Petrou, Segmentation of Color Textures, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 2, 2000, pp. 142-159.
[5] J. W. Bennett, Modeling and Analysis of Gray Tone, Color, and Multispectral Texture Images by Random Field Models and Their Generalizations, Ph.D. Dissertation, Southern Methodist University, 1997.
[6] J. W. Bennett and A. Khotanzad, Multispectral Random Field Models for Synthesis and Analysis of Color Images, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, 1998, pp. 327-332.
[7] A. Khotanzad and J. Y. Chen, Unsupervised Segmentation of Textured Images by Edge Detection in Multidimensional Features, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 11, no. 4, 1989, pp. 414-421.
[8] A. Khotanzad and A. Bouarfa, Image Segmentation by a Parallel, Non-Parametric Histogram Based Clustering Algorithm, Pattern Recognition, vol. 23, no. 9, 1990, pp. 961-963.
[9] Vision and Modeling Group, MIT Media Laboratory, Vision Texture (VisTex) database, http://wwwwhite.media.mit.edu/vismod/, 1995.
[10] Corel Corporation, Professional Photos CD-ROM Sampler – SERIES 200000, 1994.
[2] J. R. Smith and S-F. Chang, Integrated Spatial and Feature Image Query, Multimedia Systems – ACM - © Springer-Verlag 1999, vol. 7, no. 2, 1999, pp. 129-140.
[3] K. B. Eom, Segmentation of monochrome and color textures using moving average modeling approach, Elsevier Science B. V. Image and Vision Computing, no.17, 1999, pp. 233-244.
[4] M. Mirmehdi and M. Petrou, Segmentation of Color Textures, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 2, 2000, pp. 142-159.
[5] J. W. Bennett, Modeling and Analysis of Gray Tone, Color, and Multispectral Texture Images by Random Field Models and Their Generalizations, Ph.D. Dissertation, Southern Methodist University, 1997.
[6] J. W. Bennett and A. Khotanzad, Multispectral Random Field Models for Synthesis and Analysis of Color Images, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, 1998, pp. 327-332.
[7] A. Khotanzad and J. Y. Chen, Unsupervised Segmentation of Textured Images by Edge Detection in Multidimensional Features, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 11, no. 4, 1989, pp. 414-421.
[8] A. Khotanzad and A. Bouarfa, Image Segmentation by a Parallel, Non-Parametric Histogram Based Clustering Algorithm, Pattern Recognition, vol. 23, no. 9, 1990, pp. 961-963.
[9] Vision and Modeling Group, MIT Media Laboratory, Vision Texture (VisTex) database, http://wwwwhite.media.mit.edu/vismod/, 1995.
[10] Corel Corporation, Professional Photos CD-ROM Sampler – SERIES 200000, 1994.
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Published
2004-10-01
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[1]
“Color Image Segmentation Using Multispectral Random Field Texture Model & Color Content Features”, JCS&T, vol. 4, no. 03, pp. p. 141–146, Oct. 2004, Accessed: Feb. 07, 2026. [Online]. Available: https://journal.info.unlp.edu.ar/JCST/article/view/892
