Evaluation of Alpha-Trees for Hierarchical Segmentation by Horizontal Cuts

Abstract

Alpha trees, and derived $\alpha $ - $\omega $ -hierarchies are powerful tools for hierarchical image representation in computer vision. However, the quality of $\alpha $ - $\omega $ -hierarchies has not been fully evaluated, limiting their further development and application. In our study, an algorithm for evaluating the quality of $\alpha $ - $\omega $ -hierarchies based on horizontal cut filters is proposed. With the aim to automatically select optimal parameters and dissimilarity measures for $\alpha $ - $\omega $ -hierarchy constructions, key factors including maximum accuracy, construction complexity, and efficiency of $\alpha $ - $\omega $ -hierarchies are systematically considered. Notably, remote sensing images based experiments were conducted to demonstrate the usefulness of this algorithm. In addition, our algorithm can be potentially extended to qualify other types of hierarchical trees, making it useful for the automatic selection of optimal hierarchical segmentation methods.