Salient object detection is evaluated using binary ground truth (GT) with the labels being salient object class and background. In this study, the authors corroborate based on three subjective experiments on a novel image dataset that objects in natural images are inherently perceived to have varying levels of importance. The authors' dataset, named SalMoN (saliency in multi-object natural images), has 588 images containing multiple objects. The subjective experiments performed record spontaneous attention and perception through eye fixation duration, point clicking and rectangle drawing. As object saliency in a multi-object image is inherently multi-level, they propose that salient object detection must be evaluated for the capability to detect all multi-level salient objects apart from the salient object class detection capability. For this purpose, they generate multi-level maps as GT corresponding to all the dataset images using the results of the subjective experiments, with the labels being multi-level salient objects and background. They then propose the use of mean absolute error, Kendall's rank correlation and average area under precision–recall curve to evaluate existing salient object detection methods on their multi-level saliency GT dataset. Approaches that represent saliency detection on images as local-global hierarchical processing of a graph perform well in their dataset.

中文翻译：

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在具有多个级别显着性的多个对象的自然图像中评估显着对象检测

显着对象检测使用二进制地面真理（GT）进行评估，标签为显着对象类和背景。在这项研究中，作者基于对新图像数据集的三个主观实验的证实，即自然图像中的物体固有地被认为具有不同的重要性水平。作者的数据集名为SalMoN（多对象自然图像中的显着性），包含588个包含多个对象的图像。主观实验通过注视时间，定点和矩形画来记录自发的注意力和知觉。由于多对象图像中的对象显着性本质上是多级的，因此他们建议必须评估显着对象检测的能力，以检测到所有显着对象类别检测能力之外的所有多级显着对象的能力。为此，他们使用主观实验的结果生成与所有数据集图像相对应的GT多层地图，标签为多层突出对象和背景。然后，他们提出使用平均绝对误差，Kendall等级相关性和精确度-召回曲线下的平均面积来评估其多层显着性GT数据集上现有的显着物体检测方法。将图像的显着性检测表示为图形的局部全局分层处理的方法在其数据集中表现良好。在精确度-召回曲线下的等级相关性和平均面积，以评估其显着性GT数据集上现有的显着物体检测方法。将图像的显着性检测表示为图形的局部全局分层处理的方法在其数据集中表现良好。的精确度-召回曲线下的等级相关性和平均面积，以评估其显着性GT数据集上现有的显着目标检测方法。将图像的显着性检测表示为图形的局部全局分层处理的方法在其数据集中表现良好。