Tree-Pruning: A New Algorithm and Its Comparative Analysis with the Watershed Transform for Automatic Image Segmentation

Image segmentation using tree pruning (TP) and watershed (WS) has been presented in the framework of the image forest transform (IFT) - a method to reduce image processing problems related to connectivity into an optimum-path forest problem in a graph. Given that both algorithms use the IFT with similar parameters, they usually produce similar segmentation results. However, they rely on different properties of the IFT which make TP more robust than WS for automatic segmentation tasks. We propose and demonstrate an important improvement in the TP algorithm, clarify the differences between TP and WS, and provide their comparative analysis from the theoretical and practical points of view. The experiments involve automatic segmentation of license plates in a database with 990 images