CVNov 15, 2018

Psychophysical evaluation of individual low-level feature influences on visual attention

arXiv:1811.06458v14 citations
Originality Synthesis-oriented
AI Analysis

This provides a new psychophysical basis for evaluating saliency models, but it is incremental as it builds on prior experiments with synthetic stimuli.

The study analyzed how low-level visual features influence attention using synthetic images and eye-tracking from 34 participants, finding that saliency is affected by feature type, contrast, fixation timing, task difficulty, and center bias.

In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images.

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