Re-ranking of the merging order for hierarchical image segmentation

Abstract

Hierarchical image segmentation provides a set of image segmentations at different detail levels in which coarser detail levels can be produced from merges of regions belonging to finer detail levels. However, similarity measures adopted by hierarchical image segmentation methods do not always consider the homogeneity of the combined components. In this work, we propose a hierarchical graph-based image segmentation using a new similarity measure based on the variability of the merged components which is responsible for the re-ranking of the merging order that was originally established by the minimum spanning tree. Furthermore, we study how the inclusion of this characteristic has influenced the quality measures. Experiments have shown the superior performance of the proposed method on three well known image databases, and its robustness to noise was also demonstrated.