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1887

access icon free Using macroscopic information in image segmentation

© The Institution of Engineering and Technology
Received 23/02/2012, Accepted 14/12/2012, Revised 05/11/2012, Published

Post-processing ‘macroscopically’ output-segmented images obtained from conventional image segmentation (IS) techniques, leads into the concept of micro–macro IS (MMIS). MMIS pays extra attention to information extracted from relatively large image regions and as a result, overall system segmentation performance improves both subjectively and objectively. The proposed post-processing scheme is generic, in the sense that can be used together with any other existing segmentation approach. Thus given an input-segmented image, MMIS has the ability to automatically select an appropriate number of regions and classes in a way that helps object-oriented visual information to become more apparent in the final segmented output image. Computer simulation results clearly indicate that significant IS performance benefits can be obtained by augmenting conventional IS schemes within an MMIS framework, with or without input images being corrupted by additive Gaussian noise

Inspec keywords: image segmentation; AWGN

Other keywords: micro-macro image segmentation; additive Gaussian noise; computer simulation; macroscopic information; object-oriented visual information

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

References

    1. 1)
      • 7. Ren, X., Malik, J.: ‘Learning a classification model for segmentation’. Proc. Int. Conf. on Computer Vision, 2003, pp. 10–17.
    2. 2)
      • 14. Zhao, Y., George, K., Usama, F.: ‘Hierarchical clustering algorithms for document catasets’, Data Min. Knowl. Discov., 2005, 10, (2), pp. 141–168 (doi: 10.1007/s10618-005-0361-3).
    3. 3)
      • 5. Arbelaez, P., Michael, M., Charless, F., Malik, J.: ‘From contours to regions: an empirical evaluation’. Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2009, pp. 2294–230.
    4. 4)
      • 2. Felzenszwalb, P., Huttenlocher, D.: ‘Efficient graph-based image segmentation’, Int. J. Comput. Vis., 2004, 59, (2), pp. 167–181 (doi: 10.1023/B:VISI.0000022288.19776.77).
    5. 5)
      • 6. Athanasiadis, T., Mylonas, P., Avrithis, Y., Kollias, S.: ‘Semantic image segmentation and object labeling’, IEEE Trans. Circuits Syst. Video Technol., 2007, 17, (3), pp. 298–312 (doi: 10.1109/TCSVT.2007.890636).
    6. 6)
      • 15. Calderero, F., Marques, F.: ‘Region merging techniques using information theory statistical measures’, IEEE Trans. Image Process., 2010, 19, (1), pp. 1567–1586 (doi: 10.1109/TIP.2010.2043008).
    7. 7)
      • 10. Zahn, C.T.: ‘Graph-theoretic methods for detecting and describing gestalt clusters’, IEEE Trans. Comput., 1971, 20, pp. 68–86 (doi: 10.1109/T-C.1971.223083).
    8. 8)
      • 12. Unnikrishnan, R., Caroline, P., Martial, H.: ‘Toward objective evaluation of image segmentation algorithms’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (6), pp. 929–944 (doi: 10.1109/TPAMI.2007.1046).
    9. 9)
      • 4. Martin, D., Charless, F., Doron, T., Malik, J.: ‘A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics’. Proc. Int. Conf. Computer Vision., 2001, pp. 416–423.
    10. 10)
      • 16. Kim, J., John, W.F. III, Anthony, Y., Müjdat, Ç., Alan, S.W.: ‘A nonparametric statistical method for image segmentation using information theory and curve evolution’, IEEE Trans. Image Process., 2005, 14, (10), pp. 1486–1502 (doi: 10.1109/TIP.2005.854442).
    11. 11)
      • 11. Grady, L.: ‘Random walks for image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (11), pp. 1768–1783 (doi: 10.1109/TPAMI.2006.233).
    12. 12)
      • 8. Shi, J., Malik, J.: ‘Normalized cuts and image segmentation’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (8), pp. 888–905 (doi: 10.1109/34.868688).
    13. 13)
      • 3. Khan, A.A., Xydeas, C., Ahmed, H.: ‘Micro-macro image segmentation’. Proc. IADIS Int. Conf. on Computer Graphics, Visualization, Computer Vision and Image Processing, Italy, July 2011, pp. 192–197.
    14. 14)
      • 9. Cheng, H.D., Jiang, X., Sun, Y., Wang, J.: ‘Color image segmentation: advances and prospects’, Pattern Recognit., 2001, 34, (12), pp. 2259–2281 (doi: 10.1016/S0031-3203(00)00149-7).
    15. 15)
      • 17. Herbulot, A., Stéphanie, J., Stefan, D., Michel, B., Gilles, A.: ‘Segmentation of vectorial image features using shape gradients and information measures’, J. Math. Imaging Vis., 2006, 25, (3), pp. 365–386 (doi: 10.1007/s10851-006-6898-y).
    16. 16)
      • 13. Fernández, A., Gómez, S.: ‘Solving non-uniqueness in agglomerative hierarchical clustering using multidendrograms’, J. Classif., 2008, 25, (1), pp. 43–65 (doi: 10.1007/s00357-008-9004-x).
    17. 17)
      • 1. Cour, T., Florence, B., Shi, J.: ‘Spectral segmentation with multi-scale graph decomposition’. Proc. IEEE Intl. Conf. on Computer Vision and Pattern Recognition., 2005, pp. 1124–1131.
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