Thresholding in salient object detection: a survey

Abstract

Salient Object Detection (SOD) in natural images is an active research area with burgeoning applications across diverse disciplines such as object recognition, image compression, video summarization, object discovery, image retargetting etc. Most salient object detection methods model this problem as a binary segmentation problem where firstly a saliency map is found which highlights the salient pixels and suppresses the background pixels in an image. Secondly, some threshold is applied to obtain the binary segmentation from the saliency map. Thus, thresholding is an important ingredient of salient object detection methods and affects the SOD performance. In this paper, we provide a comprehensive review of various thresholding methods in literature employed for SOD. We have developed a taxonomy of thresholding methods which shall be useful to the researchers and practitioners working in this fascinating research field. Further, we also discuss unexplored thresholding approaches which can be employed in SOD. Various existing and proposed performance measures to analyze SOD methods that depend on thresholding are also presented. Experiments on popular thresholding methods have also been carried out to show the dependence of qualitative and quantitative performance on thresholding.