Saliency Preservation in Low-Resolution Grayscale Images
This addresses the problem of inefficient saliency detection models for researchers and practitioners by proposing a faster alternative, though it is incremental as it builds on prior work on low-resolution and grayscale images.
The study tackled whether visual saliency information is preserved in low-resolution grayscale images, and found that it is, leading to significant speedups in model training and detection times.
Visual salience detection originated over 500 million years ago and is one of nature's most efficient mechanisms. In contrast, many state-of-the-art computational saliency models are complex and inefficient. Most saliency models process high-resolution color (HC) images; however, insights into the evolutionary origins of visual salience detection suggest that achromatic low-resolution vision is essential to its speed and efficiency. Previous studies showed that low-resolution color and high-resolution grayscale images preserve saliency information. However, to our knowledge, no one has investigated whether saliency is preserved in low-resolution grayscale (LG) images. In this study, we explain the biological and computational motivation for LG, and show, through a range of human eye-tracking and computational modeling experiments, that saliency information is preserved in LG images. Moreover, we show that using LG images leads to significant speedups in model training and detection times and conclude by proposing LG images for fast and efficient salience detection.