  Superpixel segmentation can be used as an intermediary step in many
applications, often to improve object delineation and reduce computer workload.
However, classical methods do not incorporate information about the desired
object. Deep-learning-based approaches consider object information, but their
delineation performance depends on data annotation. Additionally, the
computational time of object-based methods is usually much higher than desired.
In this work, we propose a novel superpixel framework, named Superpixels
through Iterative CLEarcutting (SICLE), which exploits object information being
able to generate a multiscale segmentation on-the-fly. SICLE starts off from
seed oversampling and repeats optimal connectivity-based superpixel delineation
and object-based seed removal until a desired number of superpixels is reached.
It generalizes recent superpixel methods, surpassing them and other
state-of-the-art approaches in efficiency and effectiveness according to
multiple delineation metrics.
