Saliency detection is one of the most challenging problems in image analysis and computer vision. Many approaches propose different architectures based on the psychological and biological properties of the human visual attention system. However, there is still no abstract framework that summarizes the existing methods. In this paper, we offered a general framework for saliency models, which consists of five main steps: pre-processing, feature extraction, saliency map generation, saliency map combination, and post-processing. Also, we study different saliency models containing each level and compare their performance. This framework helps researchers to have a comprehensive view of studying new methods.
@article{arxiv.1912.12027,
title = {A General Framework for Saliency Detection Methods},
author = {Fateme Mostafaie and Zahra Nabizadeh and Nader Karimi and Shadrokh Samavi},
journal= {arXiv preprint arXiv:1912.12027},
year = {2022}
}
Comments
Many grammatical and spelling errors are corrected. The technical content is not changed