Automatic Image Segmentation Base on Human Color Perceptions

Full Text (PDF, 236KB), PP.25-32

Views: 0 Downloads: 0

Author(s)

Yu Li-jie 1,2,* Li De-sheng 2 Zhou Guan-ling 1

1. College of Automation Beijing Union University, Beijing, China

2. Mechanical and Electronic Technology Research Centre Beijing University of Technology, Beijing, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2009.01.04

Received: 24 Jun. 2009 / Revised: 31 Jul. 2009 / Accepted: 14 Sep. 2009 / Published: 8 Oct. 2009

Index Terms

Color image segmentation, visible color difference, region growing, human color perception

Abstract

In this paper we propose a color image segmentation algorithm based on perceptual color vision model. First, the original image is divide into image blocks which are not overlapped; then, the mean and variance of every image back was calculated in CIEL*a*b* color space, and the image blocks were divided into homogeneous color blocks and texture blocks by the variance of it. The initial seed regions are automatically selected depending on calculating the homogeneous color blocks' color difference in CIEL*a*b* color space and spatial information. The color contrast gradient of the texture blocks need to calculate and the edge information are stored for regional growing. The fuzzy region growing algorithm and coloredge detection to obtain a final segmentation map. The experimental segmentation results hold favorable consistency in terms of human perception, and confirm effectiveness of the algorithm.

Cite This Paper

Yu Li-jie,Li De-sheng,Zhou Guan-ling, "Automatic Image Segmentation Base on Human Color Perceptions", IJIGSP, vol.1, no.1, pp.25-32, 2009. DOI: 10.5815/ijigsp.2009.01.04

Reference

[1]ZHANG YU-JIN. Image project(media), image analysis. Beijing. Tsinghua University Press, 2005

[2]H. Cheng, X. Jiang, Y. Sun and J. Wang, Color image segmentation: Advances & prospects, Pat. Rec., Vol. 34, No. 12, pp. 2259-2281, Dec. 2001.

[3]J. Wu, H. Yan, and A. Chalmers, “Color image segmentation using fuzzy clustering and supervised learning”, Journal of Elec. Imag., Vol. 3, No. 4, pp. 397–403, Oct. 1994.

[4]P. Schmid, Segmentation of digitized dermatoscopic images by two-dimensional color clustering, IEEE Trans. on Med. Image., Vol. 18, No.2, pp. 164–171, Feb. 1999.

[5]Daily, M.J., J.G. Harris, K.E. Olin, K. Reiser, D.Y. Tseng, and F.M. Vilnrotter, Knowledge-based Vision Techniques Annual Technical Report. U.S. Army ETL, Fort Belvoir, VA, October, 1987.

[6]Healey, G. and T. Binford, The Role and Use of Color in a General Vision System. Proc. of the DARPA IU Workshop, Los Angeles, CA, pp. 599-613, February, 1987.

[7]GONG Sheng-rong, Digital image processing and analysis. Beijing. Tsinghua Unversity Press, 2005

[8]G.Wyszecki and W.Stiles, Color Science: Concepts and Metheds, Quantitative Data and Formulae, 2nd ed. New York: Wiley, 1982.

[9]H.D. Cheng, X.H. Jiang, Y. Sun, et al. “Color image segmentation: advances and prospects”. Pattern Recognition, 2001, pp. 2259- 2281

[10]Qi Yonghong and Zhou Shshenqi, “Review on Uniform color space and Color Difference Formula”, Print World, 2003.9:16-19.

[11]Gong Y.H, Proietti G. Image indexing and retrieval based on human perceptual color clustering. The international conference on computer vision, Munbai, 1998

[12]S. Wesolkowski, M.E. Jernigan, R.D. Dony, “Comparison of color image edge detectors in multiple color space”. ICIP-2000, pp. 796 – 799

[13]J. Maeda, V.V.Anh,T.Ishizaka and y. suzuki, “ Integration of local fractal dimension and boundary edge in segmenting natural images” , Proc. IEEE Int. Conf. on Image Processing, vol.I, pp.845-848, 1996.

[14]J. Maeda, T. Lizawa, T. Ishizaka, C. Ishikawa and Y. Suzuki, “Segmentation diffusion and linking of boundary edges”, Pattern Recognition, vol.31(12), pp.1993-1999, 1998.

[15]Ye Qixiang, Gao Wen, Wang Weiqiang, Hang Tiejun. “A Color Image Segmentation Algorithm by Using Color and Spatial Information”. Journal of Software. 2004, 15(4):522-530.

[16]Hsin-Chia Chen, Sheng-Jyh Wang. “The use of visible color difference in the quantitative evaluation of color image segmentation”. Vision, image and signal processing, IEEE proceedings. 2006, Vol.153, pp.598 - 609.