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論文名稱 Genetic cuts for image segmentation
發表日期 2014-12-17
論文收錄分類 SCI
所有作者 Shiueng Bien Yang
作者順序 第一作者
通訊作者
刊物名稱 Journal of Electronic Imaging
發表卷數 33
是否具有審稿制度
發表期數 7
期刊或學報出版地國別/地區 NATTWN-中華民國
發表年份 2000
發表月份 12
發表形式 電子期刊
所屬計劃案
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附件 JEI-14027(Gcuts).pdfJEI-14027(Gcuts).pdf


[英文摘要] :
The normalized cut (Ncut) method is a popular method for segmenting images and videos. The Ncut
method segments an image into two disjoint regions, each segmented by the same method. After the Ncut
method has been recursively applied to an image, its final segmented image is obtained. The main drawback
of the Ncut method is that a user cannot easily determine the stop criteria because users have no idea about the
number of regions in an image. This work proposes the genetic cut (Gcut) algorithm to resolve this shortcoming.
Users do need not to specify thresholds in the Gcut algorithm, which automatically segments an image into the
proper number of regions. Also, the neighbor-merging (NM) algorithm is proposed for preprocessing the images
and improves the performance of the Gcut algorithm. Thus, the proposed Gcut method combines the NM and
Gcut algorithms. Furthermore, a heuristic method is proposed to identify a good segment for the Gcut method. In
all experiments, the proposed Gcut method outperforms traditional Ncut methods.

[參考文獻] :
1. J. N. Kapur, P. K. Sahoo, and A. K. C. Wong, “A new method for graylevel
picture thresholding using the entropy of the histogram,” Comput.
Vis. Graph. Image Process. 29, 273–285 (1985).
2. S. K. Pal, R. A. King, and A. A. Hashim, “Automatic grey level thresholding
through index of fuzziness and entropy,” Pattern Recognit. Lett.
1(3), 141–146 (1983).
3. K. Chen, D. Wang, and X. Liu, “Weight adaptation and oscillatory correlation
for image segmentation,” IEEE Trans. Neural Networks 11(5),
1106–1123 (2000).
4. S. Makrogiannis, G. Economou, and S. Fotopoulos, “A region dissimilarity
relation that combines feature-space and spatial information for
color image segmentation,” IEEE Trans. Syst. Man Cybern. B, Cybern.
35(1), 44–53 (2005).
5. K. Haris et al., “Hybrid image segmentation using watersheds and fast
region merging,” IEEE Trans. Image Process. 7(12), 1684–1699
(1998).
6. G. Kanizsa, Grammatica del Vedere. Saggi su percezione e gestalt, II
Mulino, Bologna, Italy (1980).
7. B. Caselles, B. Coll, and J.-M. Miorel, “A Kanisza programme,” Progr.
Nonlinear Differential Equations Appl. 25(2), 35–55 (1996).
8. M. Wertheimer, “Laws of organization in perceptual forms (partial
translation),” in Sourcebook of Gestalt Psychology, W. B. Ellis, Ed.,
pp. 71–88, Harcourt Brace Jovanovich, Orlando, Florida (1938).
9. O. J. Morris, J. Lee, and A. G. Constantinides, “Graph theory for image
analysis: an approach based on the shortest spanning tree,” IEE Proc. F
Commun., Radar Signal Process. 133(2), 146–152 (1986).
10. P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient graph-based
image segmentation,” Int. J. Comput. Vis. 59(2), 167–181 (2004).
11. A. Moore et al., “Superpixel lattices,” in Proc. IEEE Int. Conf. on
Computer Vision and Pattern Recognition, pp. 1–8, IEEE Computer
Society, Anchorage, AK (2008).
12. O. Veksler, Y. Boykov, and P. Mehrani, “Superpixels and supervoxels in
an energy optimization framework,” in Proc. European Conf. on
Computer Vision, pp. 211–224, Springer-Verlag, Berlin, Heidelberg
(2010).
13. S. X. Yu and J. Shi, “Multiclass spectral clustering,” in Proc. IEEE Int.
Conf. on Computer Vision, pp. 313–319, IEEE Computer Society,
Pittsburgh, Pennsylvania (2003).
14. S. Wang and J. M. Siskind, “Image segmentation with ratio cut,” IEEE
Trans. Pattern Anal. Mach. Intell. 25(6), 675–690 (2003).
15. Z.-Y. Wu and R. Leahy, “An optimal graph theoretic approach to data
clustering: theory and its application to image segmentation,” IEEE
Trans. Pattern Anal. Mach. Intell. 15(11), 1101–1113 (1993).
16. J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE
Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000).
17. C.-W. Ngo, Y.-F. Ma, and H.-J. Zhang, “Video summarization and
scene detection by graph modeling,” IEEE Trans. Circuits Syst.
Video Technol. 15(2), 296–305 (2005).
18. Y. Chen, J. Z. Wang, and R. Krovetz, “CLUE: cluster-based retrieval of
images by unsupervised learning,” IEEE Trans. Image Process. 14(8),
1187–1201 (2005).
19. W. Tao, H. Jin, and Y. Zhang, “Color image segmentation based on
mean shift and normalized cuts,” IEEE Trans. Syst. Man Cybern.—
Part B 37(5), 1382–1389 (2007).
20. M. Merzougui et al., “Evolutionary image segmentation by pixel classification
and the evolutionary Xie and Beni criterion: application to
quality control,” Int. J. Comput. Intell. Inf. Secur. 2(8), 4–13 (2011).
21. S. Maji, N. K. Vishnoi, and J. Malik, “Biased normalized cuts,” in Proc.
IEEE Int. Conf. on Computer Vision and Pattern Recognition,
pp. 2057–2064, IEEE Computer Society, Providence, Rhode Island
(2011).
22. M. Paulinas and A. Uinskas, “A survey of genetic algorithms applications
for image enhancement and segmentation,” Inf. Technol. Control
36(3), 278–284 (2007).
23. A. Amelio and C. Pizzuti, “An evolutionary and graph-based method
for image segmentation,” Lec. Notes Comput. Sci. 7491, 143–152
(2012).
24. A. Amelio and C. Pizzuti, “A genetic algorithm for color image segmentation,”
Lec. Notes Comput. Sci. 7835, 314–323 (2013).
25. L. Y. Tseng and S. B. Yang, “A genetic approach to the automatic clustering
problem,” Pattern Recognit. 34, 415–424 (2001).
26. L. Y. Tseng and S. B. Yang, “A genetic clustering algorithm for data
with non-spherical-shape clusters,” Pattern Recognit. 33, 1251–1259
(2000).