Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention
Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention
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DOI：
10.3390/s20082170
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发表时间：
2020-04-01
期刊：
影响因子：
3.9
通讯作者：
Haseyama, Miki
中科院分区：
文献类型：
--
作者：
Moroto, Yuya;Maeda, Keisuke;Haseyama, Miki
提出了一种基于考虑目标和视觉注意的自适应图像选择的小镜头个性化显著性预测方法。由于预测个性化显著性图（psm）的一般方法需要大量的训练图像，因此需要建立一个使用少量训练图像的理论。为了解决这个问题，尽管找到与目标人物具有相似视觉注意力的人是有效的，但所有人都必须共同注视许多图像。因此，考虑到他们的负担就变得困难和不现实。另一方面，本文介绍了一种新的自适应图像选择（AIS）方案，该方案关注人类视觉注意与图像中物体之间的关系。AIS既关注图像中物体的多样性，也关注物体psm的多样性。具体来说，AIS对图像进行选择，使所选图像具有多种对象，保持其多样性。此外，AIS保证了人的psm的高方差，因为它代表了许多人通常注视或不注视的区域。该方法通过选择具有高多样性和方差的图像，从少量图像中选择相似的用户。这是本文的技术贡献。实验结果表明，包含新图像选择方案的个性化显著性预测是有效的。
A few-shot personalized saliency prediction based on adaptive image selection considering object and visual attention is presented in this paper. Since general methods predicting personalized saliency maps (PSMs) need a large number of training images, the establishment of a theory using a small number of training images is needed. To tackle this problem, although finding persons who have visual attention similar to that of a target person is effective, all persons have to commonly gaze at many images. Thus, it becomes difficult and unrealistic when considering their burden. On the other hand, this paper introduces a novel adaptive image selection (AIS) scheme that focuses on the relationship between human visual attention and objects in images. AIS focuses on both a diversity of objects in images and a variance of PSMs for the objects. Specifically, AIS selects images so that selected images have various kinds of objects to maintain their diversity. Moreover, AIS guarantees the high variance of PSMs for persons since it represents the regions that many persons commonly gaze at or do not gaze at. The proposed method enables selecting similar users from a small number of images by selecting images that have high diversities and variances. This is the technical contribution of this paper. Experimental results show the effectiveness of our personalized saliency prediction including the new image selection scheme.