ITSELF: Iterative Saliency Estimation fLexible Framework

TL;DR

ITSELF introduces a flexible, iterative framework for saliency detection that allows user-defined assumptions and leverages superpixel segmentation to improve accuracy across diverse image domains.

Contribution

The paper presents a novel, adaptable saliency estimation framework that integrates user-defined assumptions and superpixel-based iterative refinement for enhanced performance.

Findings

More robust than state-of-the-art methods

Competitive results on natural images

Outperforms on biomedical images

Abstract

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.