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Chin. Opt. Lett.
 Home  List of Issues    Issue 01 , Vol. 10 , 2012    10.3788/COL201210.011001

Unsupervised regions of interest extraction for color image compression
Xiaoguang Shao1;2, Kun Gao1;2, Lili Lv1;2, Guoqiang Ni1, 2
1 School of Optoelectronics, [Beijing Institute of Technology], Beijing 100081, China
2 Key Laboratory of Photoelectronic Imaging Technology and System ([Beijing Institute of Technology]),Ministry of Education, Beijing 100081, China

Chin. Opt. Lett., 2012, 10(01): pp.011001

Topic:Image processing
Keywords(OCIS Code): 100.0100  100.2000  150.0150  150.1135  

A novel unsupervised approach for regions of interest (ROI) extraction that combines the modified visual attention model and clustering analysis method is proposed. Then the non-uniform color image compression algorithm is followed to compress ROI and other regions with different compression ratios through the JPEG image compression algorithm. The reconstruction algorithm of the compressed image is similar to that of the JPEG algorithm. Experimental results show that the proposed method has better performance in terms of compression ratio and fidelity when comparing with other traditional approaches.

Copyright: © 2003-2012 . This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Posted online:2011/8/5

Get Citation: Xiaoguang Shao, Kun Gao, Lili Lv, Guoqiang Ni, , "Unsupervised regions of interest extraction for color image compression," Chin. Opt. Lett. 10(01), 011001(2012)

Note: This work was supported by the National Natural Science Foundation of China (No. 60702017) and the Scientific Research Fund of Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China (No. 2010OEIOF02).


1. U. Rutishauser, D. Walther, C. Koch, and P. Perona, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2004).

2. Q. Zhang, G. Gu, and H. Xiao, J. Multimed. 4, 363 (2009).

3. L. Zhang and K. Wang, Chin. Opt. Lett. 4, 76 (2006).

4. L. Itti, and C. Koch, Nat. Rev. Neurosci. 2, 194 (2001).

5. S. G. Mallat, IEEE Trans. Pattern Anal. Mach. Intell. 11, 674 (1989).

6. L. Itti, C. Koch, and E. Niebur, IEEE Trans. Pattern Anal. Mach. Intell. 20, 1254 (1998).

7. D. Walther, "Interactions of visual attention and object recognition: computational modeling, algorithms, and psychophysics" PhD. Thesis (California Institute of Technology, 2006).

8. J. G. Daugman, J. Opt. Soc. A 2, 1160 (1985).

9. L. Itti and C. Koch, Vis. Res. 40, 1489 (2000).

10. Y. Lu, X. Zhang, J. Kong, X. Wang, and J. Zhang, in Proceedings of Congress on Image and Signal Processing 339 (2008).

11. J. Lan and K. Shen, Chin. Opt. Lett. 8, 286 (2010).

12. X. Bian, T. Zhang, and X. Zhang, Chin. Opt. Lett. 9, 011002 (2011).

13. L. Itti and C. Koch, "iLab Image Databases",http://ilab.usc.edu/imgdbs/(2002).

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