How SLIC (Simple Linear Iterative Clustering) algorithm works

Based on the publication from Achanta et al. (2010) I created this video, to represent visually the application of the SLIC algorithms in the context of superpixel generation.

I used a RGB image by remote sensing to apply the detection of 100 superpixels. The original presentation is available at xxx, and the source-code using Python, created to make the superpixels and produce a beautiful animation, is available at https://github.com/tkorting/youtube/blob/master/slic/main.py

The original algorithm's description is as follows:

SLIC Superpixels
Authors: Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk
Abstract. 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 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.