Saliency Preservation in Low-Resolution Grayscale Images

Abstract

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.

17 pages, 8 figures

References in corpus (6)

• Video Salient Object Detection via Fully Convolutional Networks
• DeepGaze II: Reading fixations from deep features trained on object recognition
• CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research
• Scale-Invariant Convolutional Neural Networks
• Saliency Revisited: Analysis of Mouse Movements versus Fixations
• Learning of Proto-object Representations via Fixations on Low Resolution