(Elsevier, 2024) López Molina, Carlos; Iglesias Rey, Sara; Baets, Bernard de; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Quantitative image comparison has been a key topic in the image processing literature for the past 30 years.
The reasons for it are diverse, and so is the range of applications in which measures of comparison are
needed. Examples of image processing tasks requiring such measures are the evaluation of algorithmic results
(through the comparison of computer-generated results to given ground truth) or the selection of loss/goal
functions in a machine learning context. Measures of comparison in literature take different inspirations, and
are often tailored to specific needs. Nevertheless, even if some measures of comparison intend to replicate how
humans evaluate the similarity of two images, they normally overlook a fundamental characteristic of the way
humans perform such evaluation: the context of comparison. In this paper, we present a measure of comparison
for binary images that incorporates a sense of context. More specifically, we present a Methodology for the
generation of ultrametrics for context-aware comparison of binary images. We test our proposal in the context
of boundary image comparison on the BSDS500 benchmark.
