Department of Optics, University of Granada, 18071 Granada, Spain.
Sensors (Basel). 2021 Feb 1;21(3):970. doi: 10.3390/s21030970.
Saliency prediction is a very important and challenging task within the computer vision community. Many models exist that try to predict the salient regions on a scene from its RGB image values. Several new models are developed, and spectral imaging techniques may potentially overcome the limitations found when using RGB images. However, the experimental study of such models based on spectral images is difficult because of the lack of available data to work with. This article presents the first eight-channel multispectral image database of outdoor urban scenes together with their gaze data recorded using an eyetracker over several observers performing different visualization tasks. Besides, the information from this database is used to study whether the complexity of the images has an impact on the saliency maps retrieved from the observers. Results show that more complex images do not correlate with higher differences in the saliency maps obtained.
显著度预测是计算机视觉领域中一个非常重要且具有挑战性的任务。许多模型试图根据场景的 RGB 图像值来预测显著区域。已经开发了一些新的模型，并且光谱成像技术可能有潜力克服使用 RGB 图像时发现的局限性。然而，由于缺乏可用的数据来进行此类模型的实验研究，因此基于光谱图像的此类模型的实验研究是困难的。本文提出了第一个具有 8 个通道的多光谱户外城市场景图像数据库，以及使用眼动追踪器在多个观察者执行不同可视化任务时记录的注视数据。此外，还利用该数据库的信息来研究图像的复杂性是否会对从观察者那里获取的显著度图产生影响。结果表明，更复杂的图像并不与获得的显著度图之间的更高差异相关。
