Visual Saliency Prediction - Models and Evaluation: Analyzing models and evaluation metrics for predicting visual saliency, i.e., identifying the most relevant regions in images or videos

Authors

  • Dr. Agata Grabowska Associate Professor of Computer Science, Wrocław University of Science and Technology, Poland Author

Keywords:

Visual Saliency Prediction, Models

Abstract

Visual saliency prediction plays a crucial role in various computer vision applications by identifying the most relevant regions in images or videos. This paper presents a comprehensive review of models and evaluation metrics for visual saliency prediction. We discuss the evolution of saliency prediction models, from early bottom-up approaches to the latest deep learning-based methods. Additionally, we analyze the various evaluation metrics used to assess the performance of these models. Through a comparative study, we highlight the strengths and weaknesses of different models and metrics, providing insights into the current state-of-the-art in visual saliency prediction. Finally, we discuss future research directions and challenges in this field.

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Published

10-06-2022

How to Cite

[1]
Dr. Agata Grabowska, “Visual Saliency Prediction - Models and Evaluation: Analyzing models and evaluation metrics for predicting visual saliency, i.e., identifying the most relevant regions in images or videos”, J. of Artificial Int. Research and App., vol. 2, no. 2, pp. 99–106, Jun. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/165

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