Salient object detection using HOS based L0 smoothing and shape-aware region merging

Recent salient object detection algorithms often involve a segmentation step to produce saliency maps preserving boundaries. However, over-segmented results that many segmentation methods produce confuse to describe object boundaries. In this paper, we present a novel salient object detection algorithm which produces reliable salient object candidates. First, the input image is processed by Higher Order Statistics (HOS) based L₀ smoothing to highlight strong edges and reduce texture. We then apply image segmentation to the HOS L₀ smoothed image to produce improved results in which the number of over-segmented regions is greatly reduced. Second, we propose shape-aware region merging with a novel region scale measure. Finally, a saliency map from the merging result is generated by taking two simple saliency cues. Extensive experiments on a challenging saliency dataset indicate that our algorithm has comparable performance against state-of-the-arts.