Comparing Salient Object Detection Results without Ground Truth

A wide variety of methods have been developed to approach the problem of salient object detection. The performance of these methods is often image-dependent. This paper aims to develop a method that is able to select for an input image the best salient object detection result from many results produced by different methods. This is a challenging task as different salient object detection results need to be compared without any ground truth. This paper addresses this challenge by designing a range of features to measure the quality of salient object detection results. These features are then used in various machine learning algorithms to rank different salient object detection results. Our experiments show that our method is promising for ranking salient object detection results and our method is also able to pick the best salient object detection result such that the overall salient object detection performance is better than each individual method.