Salient object detection using HOS based L0 smoothing and shape-aware region merging
Hyunjun Eun, Jonghee Kim, Changick Kim · 2015
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 L0smoothing to highlight strong edges and reduce texture. We then apply image segmentation to the HOS L0smoothed 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.