Iterative Saliency Enhancement over Superpixel Similarity

Leonardo Joao, Leonardo Joao, Alexandre Falcao

2024

Abstract

Saliency Object Detection (SOD) has several applications in image analysis. The methods have evolved from image-intrinsic to object-inspired (deep-learning-based) models. However, when a model fails, there is no alternative to enhance its saliency map. We fill this gap by introducing a hybrid approach, the Iterative Saliency Enhancement over Superpixel Similarity (ISESS), that iteratively generates enhanced saliency maps by executing two operations alternately: object-based superpixel segmentation and superpixel-based saliency estimation - cycling operations never exploited. ISESS estimates seeds for superpixel delineation from a given saliency map and defines superpixel queries in the foreground and background. A new saliency map results from color similarities between queries and superpixels at each iteration. The process repeats, and, after a given number of iterations, the generated saliency maps are combined into one by cellular automata. Finally, the resulting map is merged with the initial one by the maximum between their average values per superpixel. We demonstrate that our hybrid model consistently outperforms three state-of-the-art deep-learning-based methods on five image datasets.

Paper Citation

in Harvard Style

Joao L. and Falcao A. (2024). Iterative Saliency Enhancement over Superpixel Similarity. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 298-308. DOI: 10.5220/0012305800003660

in Bibtex Style

@conference{visapp24,
author={Leonardo Joao and Alexandre Falcao},
title={Iterative Saliency Enhancement over Superpixel Similarity},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={298-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012305800003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Iterative Saliency Enhancement over Superpixel Similarity
SN - 978-989-758-679-8
AU - Joao L.
AU - Falcao A.
PY - 2024
SP - 298
EP - 308
DO - 10.5220/0012305800003660
PB - SciTePress