An image is decomposed into small patches whose features, e.g., average RGB color, are clustered in feature space using binning in a Cartesian lattice. The general assumption is that patches in the same bin are similar according to the similarity metric dictated by the feature and bin size. By changing the feature and bin size parameters, we can achieve different effects which can be used for segmentation, and hierarchical image segmentation as shown in the videos below.