Visual attention prediction for images with leading line structure

Abstract

Researchers have proposed a wide variety of visual attention models, ranging from models that use local, low-level image features to recent approaches that incorporate semantic information. However, most models do not account for the visual attention evident in images with certain global structures. We focus specifically on “leading line” structures, in which explicit or implicit lines converge at a point. Through this study, we have conducted the experiments to investigate the visual attentions in images with leading line structure and propose new models that combine the low-level feature of center-surround differences of visual stimuli, the semantic feature of center bias and the structure feature of leading lines. We also create a new data set from 110 natural images containing leading lines and the eye-tracking data for 16 subjects. Our evaluation experiment showed that our models outperform the existing models against common indicators of saliency-map evaluation, underscoring the importance of leading lines in the modeling of visual attention.