access icon free Image segmentation framework based on multiple feature spaces

Image segmentation plays a key role in many fields such as image processing and recognition. Although various segmentation methods have been proposed in recent decades, most of these methods are based on only a single feature space. How to combine various features to image segmentation is a challenge problem. To address this problem, the authors propose to combine different features based on evolutionary multiobjective optimisation. Two optimisation objectives, which are based on colour and texture features, respectively, are therefore designed for image segmentation. The experiments show that the author's method is able to combine multiple features for image segmentation successfully.

Inspec keywords: feature extraction; evolutionary computation; image segmentation

Other keywords: single feature space; image recgonition; colour features; image processing; multiple feature spaces; image segmentation framework; evolutionary multiobjective optimisation; texture features

Subjects: Optical, image and video signal processing; Optimisation techniques; Computer vision and image processing techniques; Optimisation techniques

References

    1. 1)
    2. 2)
    3. 3)
      • 4. Shapiro, L.G., Stockman, G.C.: ‘Computer vision’ (The New Jersey Press, 2001).
    4. 4)
    5. 5)
      • 17. Zhao, J., Han, C.Z., Wei, B.: ‘Binary particle swarm optimization with multiple evolutionary strategies’, Sci. Chin F, 2012, 55, (11), pp. 24852494.
    6. 6)
    7. 7)
      • 10. Belongie, S., Carson, C., Greenspan, H.: ‘Color-and texture-based image segmentation using em and its application to content-based image retrieval’. Proc. of the Sixth Int. Conf. on Computer Vision, 1998, 675682.
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
      • 11. Everitt, B.S.: ‘Cluster Analysis’ (The Wiley Press, 2011, 5th edn.).
    19. 19)
    20. 20)
      • 15. Deb, K.: ‘Multi-objective optimization using evolutionary algorithms’ (The Wiley Press, 2001).
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
      • 26. Shirakawa, S., Nagao, T.: ‘Evolutionary image segmentation based on multiobjective clustering’, IEEE Trans. Evol. Comput., Trondheim, Norway, May2009, pp. 24662473.
    28. 28)
    29. 29)
      • 2. Economou, G., Fotinos, A., Makrogiannis, S., Fotopoulos, S.: ‘Color image edge detection based on nonparametric density estimation’. Proc. Int. Conf. on Image Processing, October 2001, pp. 922925.
    30. 30)
    31. 31)
    32. 32)
    33. 33)
    34. 34)
    35. 35)
    36. 36)
    37. 37)
    38. 38)
    39. 39)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2014.0236
Loading

Related content

content/journals/10.1049/iet-ipr.2014.0236
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading