ITSELF: Iterative Saliency Estimation fLexible Framework

• URL: http://arxiv.org/abs/2006.16956v2
• Date: Fri, 24 Jul 2020 19:16:37 GMT
• Title: ITSELF: Iterative Saliency Estimation fLexible Framework
• Authors: Leonardo de Melo Joao, Felipe de Castro Belem, Alexandre Xavier Falcao
• Abstract summary: Saliency object detection estimates the objects that most stand out in an image. We propose a superpixel-based ITerative Saliency Estimation fLexible Framework (ITSELF) that allows any user-defined assumptions to be added to the model. We compare ITSELF to two state-of-the-art saliency estimators on five metrics and six datasets.
• Score: 68.8204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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