Driven by business intelligence applications for rating attraction of products in shops, a new problem — salient object grading is studied in this paper. In computer vision, plenty of salient object detection approaches have been proposed, while most existing studies detect objects in a binary manner: salient or not. This paper focuses on a new problem setting that requires detecting all salient objects and categorizing them into different salient levels. Based on that, a pixel-wise ordinal classification method is proposed. It consists of a multi-resolution saliency detector which detects and segments objects, an ordinal classifier which grades pixels into different salient levels, and a binary saliency enhancer which sharpens the difference between non-saliency and all other salient levels. Two new image datasets with salient level labels are constructed. Experimental results demonstrate that, on the one hand, the proposed method provides effective salient level predictions and on the other hand, offers very comparable performance with state-of-the-art salient object detection methods in the traditional problem setting.

在商业智能应用程序对商店中产品的吸引力评估的驱动下，本文研究了一个新问题-显着对象分级。在计算机视觉中，已经提出了许多显着的物体检测方法，而大多数现有研究以二进制方式检测物体：显着或不显着。本文关注于一个新的问题设置，该问题需要检测所有显着的对象并将它们分类为不同的显着级别。在此基础上，提出了一种像素序数分类方法。它包括一个检测和分割对象的多分辨率显着性检测器，一个将像素划分为不同显着性等级的序数分类器，以及一个使非显着性与所有其他显着性等级之间的差异更加明显的二进制显着性增强器。构造了两个带有显着级别标签的新图像数据集。实验结果表明，一方面，该方法提供了有效的显着水平预测，另一方面，与传统问题环境中的最新显着目标检测方法相比，具有非常可比的性能。