  Saliency object detection estimates the objects that most stand out in an
image. The available unsupervised saliency estimators rely on a pre-determined
set of assumptions of how humans perceive saliency to create discriminating
features. By fixing the pre-selected assumptions as an integral part of their
models, these methods cannot be easily extended for specific settings and
different image domains. We then propose a superpixel-based ITerative Saliency
Estimation fLexible Framework (ITSELF) that allows any user-defined assumptions
to be added to the model when required. Thanks to recent advancements in
superpixel segmentation algorithms, saliency-maps can be used to improve
superpixel delineation. By combining a saliency-based superpixel algorithm to a
superpixel-based saliency estimator, we propose a novel saliency/superpixel
self-improving loop to iteratively enhance saliency maps. We compare ITSELF to
two state-of-the-art saliency estimators on five metrics and six datasets, four
of which are composed of natural-images, and two of biomedical-images.
Experiments show that our approach is more robust than the compared methods,
presenting competitive results on natural-image datasets and outperforming them
on biomedical-image datasets.
