Line Drawings of Natural Scenes Guide Visual Attention

TL;DR

This study demonstrates that line drawings of natural scenes can effectively guide visual attention, highlighting the importance of Gestalt features and scene layout in rapid scene perception and object search.

Contribution

The paper provides empirical evidence that line drawings, lacking color and luminance, still guide visual attention similarly to natural images, and introduces a computational model leveraging this insight.

Findings

Fixation distributions on line drawings correlate highly with natural images.

Subjects focus more on closed regions in line drawings, linked to dominant objects.

A computational model using line drawings improves fixation prediction performance.

Abstract

Visual search is an important strategy of the human visual system for fast scene perception. The guided search theory suggests that the global layout or other top-down sources of scenes play a crucial role in guiding object searching. In order to verify the specific roles of scene layout and regional cues in guiding visual attention, we executed a psychophysical experiment to record the human fixations on line drawings of natural scenes with an eye-tracking system in this work. We collected the human fixations of ten subjects from 498 natural images and of another ten subjects from the corresponding 996 human-marked line drawings of boundaries (two boundary maps per image) under free-viewing condition. The experimental results show that with the absence of some basic features like color and luminance, the distribution of the fixations on the line drawings has a high correlation with that on the natural images. Moreover, compared to the basic cues of regions, subjects pay more attention to the closed regions of line drawings which are usually related to the dominant objects of the scenes. Finally, we built a computational model to demonstrate that the fixation information on the line drawings can be used to significantly improve the performances of classical bottom-up models for fixation prediction in natural scenes. These results support that Gestalt features and scene layout are important cues for guiding fast visual object searching.