Superpixels algorithms through network community detection
Anthony Perez
Community detection is a powerful tool from complex networks analysis that
finds applications in various research areas. Several image segmentation
methods rely for instance on community detection algorithms as a black box in
order to compute undersegmentations, i.e. a small number of regions that
represent areas of interest of the image. However, to the best of our
knowledge, the efficiency of such an approach w.r.t. superpixels, that aim at
representing the image at a smaller level while preserving as much as possible
original information, has been neglected so far. The only related work seems to
be the one by Liu et. al. (IET Image Processing, 2022) that developed a
superpixels algorithm using a so-called modularity maximization approach,
leading to relevant results. We follow this line of research by studying the
efficiency of superpixels computed by state-of-the-art community detection
algorithms on a 4-connected pixel graph, so-called pixel-grid. We first detect
communities on such a graph and then apply a simple merging procedure that
allows to obtain the desired number of superpixels. As we shall see, such
methods result in the computation of relevant superpixels as emphasized by both
qualitative and quantitative experiments, according to different widely-used
metrics based on ground-truth comparison or on superpixels only. We observe
that the choice of the community detection algorithm has a great impact on the
number of communities and hence on the merging procedure. Similarly, small
variations on the pixel-grid may provide different results from both
qualitative and quantitative viewpoints. For the sake of completeness, we
compare our results with those of several state-of-the-art superpixels
algorithms as computed by Stutz et al. (Computer Vision and Image
Understanding, 2018).