A Graph-Based Hierarchical Image Segmentation Method Based on a Statistical Merging Predicate

Abstract

Hierarchical image segmentation provides a set of image segmentations at different detail levels in which coaser details levels can be produced by simple merges of regions from segmentations at finer detail levels. Most image segmentation algorithms, such as region merging algorithms, rely on a criterion for merging that does not lead to a hierarchy. In addition, for image segmentation, the tuning of the parameters can be difficult. In this work, we propose a hierarchical graph-based image segmentation relying on a statistical region merging. Furthermore, we study how the inclusion of hierarchical property have influenced the computation of quality measures in the original method. Quantitative and qualitative assessments of the method on two image databases show efficiency and ease of use of our method.