Algorithms for hierarchical segmentation based on the Felzenszwalb-Huttenlocher dissimilarity

Hierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy. Guimaraes et al. proposed in 2012 a hierarchical graph-based image segmentation method relying on a criterion popularized by Felzenszwalb and Huttenlocher in 2004, hence hierarchizing the popular Felzenszwalb-Huttenlocher method. However, Guimaraes et al. did not provide an algorithm to compute the proposed hierarchy. We propose a series of algorithms to compute the result of this hierarchical graph-based image segmentation method. For an image of size 321 × 481 pixels, the most efficient algorithm produces the result in half a second whereas the most naive one requires more than four hours