Abstract
Dividing the image into superpixels contributes to further processing of the image. Simple linear iterative clustering (SLIC) algorithm achieves good segmentation result by clustering color and distance characteristics of pixels. However, finite superpixels easily cause under-segmentation. Therefore, the work corrects segmentation result of SLIC by k-means clustering method calculating similarity based on weighted Euclidean distance. After that, the under-segmentation superpixel blocks are conducted with k-means clustering based on binary classification. Result shows that the corrected SLIC segmentation has better visual effect and index.
Similar content being viewed by others
References
Fulkerson, B., Vedaldi, A., Soatto, S.: Class segmentation and object localization with superpixel neighborhoods. Proceedings 30(2), 670–677 (2009)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Felzenszwalb, P.F.: Efficient graph-based image segmentation. Int. J. Comput. Vis. (IJCV) 59(2), 167–181 (2004)
Moore, A.P., et al.: Superpixel lattices. IEEE Comput. Vis. Pattern Recognit. (CVPR) 2008, 1–8 (2008)
Veksler, O., Boykov, Y., Mehrani, P.: Superpixels and supervoxels in an energy optimization framework. Proceedings of Computer Vision—ECCV, European Conference on Computer Vision, Heraklion, Crete, Greece, September 5–11, pp. 211–224 (2010)
Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based onimmersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward featurespace analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Levinshtein, A., et al.: TurboPixels: fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2290–2297 (2009)
Achanta, R., et al.: SLIC superpixels. Epfl (2010)
Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a K-means clustering algorithm. Appl. Stat. 28(1), 100–108 (1979)
Leon, K., et al.: Color measurement in L*a*b* units from RGB digital images. Food Res. Int. 39(10), 1084–1091 (2006)
Martin, D., et al.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. ICCV 2001. Proceedings of Eighth IEEE International Conference on Computer Vision, vol. 2, pp. 416–423. IEEE (2001)
Van Den Bergh, M., et al.: SEEDS: superpixels extracted via energy-driven sampling. Int. J. Comput. Vis. 111(3), 1–17 (2015)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Han, Cy. Improved SLIC imagine segmentation algorithm based on K-means. Cluster Comput 20, 1017–1023 (2017). https://doi.org/10.1007/s10586-017-0792-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-0792-9