Abstract
Video object segmentation is a task that humans perform
efficiently and effectively, but which is difficult for a computer
to perform. Since video segmentation plays an important role for
many emerging applications, as those enabled by the MPEG-4 and
MPEG-7 standards, the ability to assess the segmentation quality
in view of the application targets is a relevant task for which a
standard, or even a consensual, solution is not available. This
paper considers the evaluation of overall segmentation partitions
quality, highlighting one of its major components: the contextual
relevance of the segmented objects. Video object relevance metrics
are presented taking into account the behaviour of the human
visual system and the visual attention mechanisms. In particular,
contextual relevance evaluation takes into account the context
where an object is found, exploiting, for instance, the contrast to
neighbours or the position in the image. Most of the relevance
metrics proposed in this paper can also be used in contexts other
than segmentation quality evaluation, such as object-based rate
control algorithms, description creation, or image and video
quality evaluation.