Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk
Superpixels are becoming increasingly popular for use in
computer vision applications. However, there are few algorithms that
output a desired number of regular, compact superpixels with a low computational
overhead. We introduce a novel algorithm called SLIC (Simple Linear Iterative Clustering) that clusters pixels
in the combined five-dimensional color and image plane space to efficiently generate compact, nearly uniform superpixels. The simplicity of
our approach makes it extremely easy to use - a lone parameter specifies
the number of superpixels - and the efficiency of the algorithm makes it
very practical. Experiments show that our approach produces superpixels
at a lower computational cost while achieving a segmentation quality
equal to or greater than four state-of-the-art methods, as measured by
boundary recall and under-segmentation error. We also demonstrate the
benefits of our superpixel approach in contrast to existing methods for
two tasks in which superpixels have already been shown to increase performance
over pixel-based methods.
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk, SLIC Superpixels
, EPFL Technical Report no. 149300, June 2010.
Download Windows executable (GUI)
Windows GUI based executable
The much awaited C++ source code is now available for download (includes SLIC supervoxels code)!
MS Visual Studio 2008 workspace
(with a few bugs removed - 23 March 2011)
Sample segmentation output
[Click on the images to see bigger versions.]
Visual Comparison with other algorithms
[GS04] Graph-based segmentation
[NC05] Normalized cuts
Other superpixel methods
[GS04] Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. IJCV (2004).
[NC05] G. Mori, Guiding Model Search Using Segmentation. ICCV (2005).
[TP09] Levinshtein, A., Stere, A., Kutulakos, K., Fleet, D., Dickinson, S., Siddiqi, K.:Turbopixels: Fast superpixels using geometric flows. PAMI (2009)
[QS09] Vedaldi, A., Soatto, S.: Quick shift and kernel methods for mode seeking. ECCV (2008)
Work that uses SLIC superpixels
A. Lucchi, K. Smith, R. Achanta, V. Lepetit and P. Fua, A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images
, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Beijing, China, 2010.
New! Check out the zero parameter SLICO (or "SLIC zero) algorithm.
The number of desired superpixels is the ONLY value to input! Superpixels have never been this easy and pretty.
Win32 GUI based executable (no source code)
DISCLAIMER: Please use the software provided on this page at your own risk. The executable is provided only for the purpose of evalualtion of the algorithm presented in the paper "SLIC Superpixels Compared to State-of-the-art Superpixel Methods" (TPAMI 2012). Neither the authors of the paper nor EPFL can be held responsible for any damages resulting from use of this software.