APSIPA Transactions on Signal and Information Processing > Vol 13 > Issue 1

GreenSaliency: A Lightweight and Efficient Image Saliency Detection Method

Zhanxuan Mei, University of Southern California, USA, zhanxuan@usc.edu , Yun-Cheng Wang, University of Southern California, USA, C.-C. Jay Kuo, University of Southern California, USA
 
Suggested Citation
Zhanxuan Mei, Yun-Cheng Wang and C.-C. Jay Kuo (2024), "GreenSaliency: A Lightweight and Efficient Image Saliency Detection Method", APSIPA Transactions on Signal and Information Processing: Vol. 13: No. 1, e19. http://dx.doi.org/10.1561/116.20240023

Publication Date: 10 Sep 2024
© 2024 Z. Mei, Y.-C. Wang and C.-C. J. Kuo
 
Subjects
Image and video processing,  Statistical/Machine learning
 
Keywords
Image Saliency DetectionHuman GazeGreen Learning
 

Share

Open Access

This is published under the terms of CC BY-NC.

Downloaded: 117 times

In this article:
Introduction 
Related Work 
Proposed GreenSaliency Method 
Experiments 
Conclusion and Future Work 
References 

Abstract

Image saliency detection is crucial in understanding human gaze patterns from visual stimuli. The escalating demand for research in image saliency detection is driven by the growing necessity to incorporate such techniques into various computer vision tasks and to understand human visual systems. Many existing image saliency detection methods rely on deep neural networks (DNNs) to achieve good performance. However, the high computational complexity associated with these approaches impedes their integration with other modules or deployment on resource-constrained platforms, such as mobile devices. To address this, we propose a novel image saliency detection method named GreenSaliency, which has a small model size, minimal carbon footprint, and low computational complexity. GreenSaliency can be a competitive alternative to the existing deep-learning-based (DL-based) image saliency detection methods with limited computation resources. GreenSaliency comprises two primary steps: 1) multi-layer hybrid feature extraction and 2) multipath saliency prediction. Experimental results demonstrate that GreenSaliency achieves comparable performance to the state-ofthe- art DL-based methods while possessing a considerably smaller model size and significantly reduced computational complexity.

DOI:10.1561/116.20240023