R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Süsstrunk. SLIC Superpixels Compared to State-Of-The-Art Superpixel Methods. Transactions on Pattern Analysis and Machine Intelligence, pages 2274 – 2282, 2012.
Achanta et al. proposed their superpixel algorithm "Simple Linear Iterative Clustering", short SLIC, in [1]. The above paper compares SLIC to several superpixel algorithms with respect to Boundary Recall, Undersegmentation Error and runtime: Normalized Cuts [2], the approach proposed by Felzenswalb and Huttenlocher [3], Quick Shift [4], Watersheds [5] and Turbopixels [6].
Figure 1: Superpixel segmentations with roughly $600$ superpixels generated by the original implementation of SLIC which allows to adjust the compactness of the superpixels. The images are taken from the validation set of the Berkeley Segmentation Dataset [8]. From top to bottom: compactness set to $1$; compactness set to $10$; compactness set to $40$.
[1] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk. SLIC Superpixels. Technical report, EPFL, Lausanne, 2010.
[2] X. Ren, J. Malik. Learning a classification model for segmentation. Proceedings of the International Conference on Computer Vision, pages 10–17, 2003.
[3] P. F. Felzenswalb, D. P. Huttenlocher. Efficient graph-based image segmentation. International Journal of Computer Vision, volume 59, number 2, 2004.
[4] A. Vedaldi, S. Soatto. Quick shift and kernel methods for mode seeking. Proceedings of the European Conference on Computer Vision, pages 705–718, 2008.
[5] L. Vincent, P. Soille. Watersheds in digital spaces: An efficient algorithm based on immersion simulations, Transactions on Pattern Analysis and Machine Intelligence, volume 13, number 6, pages 583-598, 1991.
[6] A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, K. Siddiqi. TurboPixels: Fast superpixels using geometric flows. Transactions on Pattern Analysis and Machine Intelligence, volume 31, number 12, pages 2290–2297, 2009.
[7] A. Vedaldi, B. Fulkerson. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/, 2008.
[8] P. Arbeláez, M. Maire, C. Fowlkes, J. Malik. Contour detection and hierarchical image segmentation. Transactions on Pattern Analysis and Machine Intelligence, volume 33, number 5, pages 898–916, 2011.
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Achanta et al. proposed their superpixel algorithm "Simple Linear Iterative Clustering", short SLIC, in [1]. The above paper compares SLIC to several superpixel algorithms with respect to Boundary Recall, Undersegmentation Error and runtime: Normalized Cuts [2], the approach proposed by Felzenswalb and Huttenlocher [3], Quick Shift [4], Watersheds [5] and Turbopixels [6].
Superpixel segmentations generated by the original implementation of SLIC can be found in figure 1. Additionally, the VLFeat Library [7] provides an implementation of SLIC, see my article on running VLFeat's implementation of SLIC using C++ and CMake.Update. Thorough evaluation of both implementations can be found in my bachelor thesis: Bachelor Thesis “Superpixel Segmentation Using Depth Information”.