GreenSaliency: A Lightweight and Efficient Image Saliency Detection Method
Year: 2024
Type: article
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) multi-path saliency prediction. Experimental results demonstrate that GreenSaliency achieves comparable performance to the state-of-the-art DL-based methods while possessing a considerably smaller model size and significantly reduced computational complexity.
DOI: 10.1561/116.20240023
Authors: Zhanxuan Mei, Yun-Cheng Wang, C.-C. Jay Kuo
Institution: University of Southern California
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,000
OpenAlex ID: W4402408059