Abstract

This paper addresses the problem of the supervised assessment of hi- erarchical region-based image representations. Given the large amount of par- titions represented in such structures, the supervised assessment approaches in the literature are based on selecting a reduced set of representative partitions and evaluating their quality. Assessment results, therefore, depend on the partition se- lection strategy used. Instead, we propose to find the partition i n the tree that best matches the ground-truth partition, that is, the upper-bound partition selection. We show that different partition selection algorithms can lead to different conclu- sions regarding the quality of the assessed trees and that the upper-bound partition selection provides the following advantages: 1) it does not limit the assessment to a reduced set of partitions, and 2) it better discriminates the random trees from actual ones, which reflects a better qualitative behavior. We model the problem as a Linear Fractional Combinatorial Optimization (LFCO) problem, which makes the upper-bound selection feasible and efficient.