Predicting the human visual attention in an image is called saliency prediction and is an active research area in the field of neuroscience and computer vision. Early works on saliency prediction was performed by using low‐level features. In recent years, convolutional neural networks have been adapted for saliency prediction and achieved the state‐of‐the‐art performance. However, the eye‐gaze depends on the personality of each viewer and conventional methods did not take into account such individual properties of the viewer. Therefore, this paper proposes a novel saliency prediction method considering the influence of eye‐gaze. Assuming that personality can be expressed as the degree of attention to an object, our proposed method considers the personality by learning which objects are likely to be perceived by each viewer and weighting the universal saliency map with the generated mask based on the object detection results. The experimental results show that the proposed universal saliency map achieves higher accuracy than conventional methods on the public dataset, and the proposed weighted saliency map can reflect the variation of the eye‐gaze influences among viewers.

中文翻译：

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基于目标识别和注视分析的显着性预测

预测图像中的人类视觉注意力称为显着性预测，并且是神经科学和计算机视觉领域中活跃的研究领域。显着性预测的早期工作是通过使用低层功能进行的。近年来，卷积神经网络已针对显着性预测进行了调整，并实现了最先进的性能。但是，视线取决于每个观看者的个性，传统方法没有考虑到观看者的这种个性。因此，本文提出了一种考虑视线影响的显着性预测方法。假设人格可以表示为对物体的关注程度，我们提出的方法通过了解每个观看者可能会感知到哪些对象，并根据对象检测结果对生成的模板与通用显着图进行加权，来考虑个性。实验结果表明，所提出的通用显着性图在公共数据集上比常规方法具有更高的准确性，并且所提出的加权显着性图可以反映出观众之间视线影响的变化。