  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 need,
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.
