Mori, Guiding Model Search Using Segmentation. Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Please note: for questions related to these implementations please contact the authors/owners directly. The following are links to other implementations of SLIC, not developed at EPFL. Mex file and MATLAB demo script Links to other SLIC implementations MS Visual Studio 2008 workspace MATLAB code for SLIC and SLICO superpixels, and SLIC supervoxels Linux executable (64 bits) The C++ source code and executable for SLIC superpixels and supervoxels available here: Linux executable (32 bits) Download 64 bits Linux executable Windows command line based executable Download 32 bits Linux executable Windows GUI based executable Download Windows executable (Command line) Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels, EPFL Technical Report no. Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels Compared to State-of-the-art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. This page also provides C++ code for SLICO, the zero parameter version of the SLIC algorithm. This page provides C++ source code for SLIC superpixels and supervoxels. 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. 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. 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. 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. However, there are few algorithms that output a desired number of regular, compact superpixels with a low computational overhead. Superpixels are becoming increasingly popular for use in computer vision applications.

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