The visual saliency models based on low-level features of an image have the problem of low accuracy and scalability, while the visual saliency models based on deep neural networks can effectively improve the prediction performance, but require a large amount of training data, e.g., eye tracking data, to achieve good results. However, the traditional eye tracking method is limited by high equipment and time cost, complex operation process, low user experience, etc. Therefore, this paper proposed a visual saliency model based on crowdsourcing eye tracking data, which was collected by gaze recall with self-reporting from crowd workers. Parameter optimization on our crowdsourcing method was explored, and it came out that the accuracy of gaze data reached 1° of visual angle, which was 3.6% higher than other existed crowdsourcing methods. On this basis, we collected a webpage dataset of crowdsourcing gaze data and constructed a visual saliency model based on a fully convolutional neural network (FCN). The evaluation results showed that after trained by crowdsourcing gaze data, the model performed better, such as prediction accuracy increased by 44.8%. Also, our model outperformed the existing visual saliency models. We also applied our model to help webpage designers evaluate and revise their visual designs, and the experimental results showed that the revised design obtained improved ratings by 8.2% compared to the initial design.

基于图像低级特征的视觉显着性模型存在精度低和可扩展性低的问题，而基于深度神经网络的视觉显着性模型可以有效地提高预测性能，但需要大量的训练数据，例如眼动追踪数据，取得良好效果。然而，传统的眼动追踪方法受设备，时间成本高，操作过程复杂，用户体验低等方面的局限。因此，本文提出了一种基于众包眼动追踪数据的视觉显着性模型，该模型是通过自我凝视回想收集的-人群工作者的报告。对我们的众包方法进行了参数优化，结果表明，凝视数据的准确性达到了视角的1°，比其他现有的众包方法高3.6％。在此基础上，我们收集了众包凝视数据的网页数据集，并基于全卷积神经网络（FCN）构建了可视显着性模型。评估结果表明，经过众包注视数据训练后，该模型表现较好，预测精度提高了44.8％。此外，我们的模型优于现有的视觉显着性模型。我们还应用了该模型来帮助网页设计师评估和修改其视觉设计，实验结果表明，与初始设计相比，修改后的设计获得了8.2％的改进评级。如预测准确性提高了44.8％。此外，我们的模型优于现有的视觉显着性模型。我们还应用了该模型来帮助网页设计师评估和修改其视觉设计，实验结果表明，与初始设计相比，修改后的设计获得了8.2％的改进评级。如预测准确性提高了44.8％。此外，我们的模型优于现有的视觉显着性模型。我们还应用了该模型来帮助网页设计师评估和修改其视觉设计，实验结果表明，与初始设计相比，修改后的设计获得了8.2％的改进评级。