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This paper addresses the problem of the supervised assessment of hierarchical
region-based image representations. Given the large amount of partitions
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 selection
strategy used. Instead, we propose to find the partition in 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 conclusions
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.
CitationPont, J., Marques, F. Supervised assessment of segmentation hierarchies. A: European Conference on Computer Vision. "Lecture Notes in Computer Science". Florència: Springer, 2012, p. 814-827.
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