Salient Object Detection via Random Forest
Shuze Du, Shifeng Chen · IEEE Signal Processing Letters · 2013
Salient object detection plays an important role in image pre-processing. Existing approaches often neglect the contours of salient objects, thus resulting in inaccurate detection for large objects. Besides, they mainly focus on detecting only a single object. In this paper, we detect the salient object from the view of the object contour. We propose to exploit the random forest to measure patch rarities and compute similarities among patches. A global rarity map is calculated based on the patch's rareness over the whole image. The approximate contour of the salient object is extracted based on this rarity map by using an active contour model. Next, a local saliency map is obtained by the similarities of patches inside the contour and those outside. Finally, the local map is refined through image segmentation. Our method can detect not only a single object but also multiple objects. Experimental evaluation on the ASD-1000 and SED2 datasets shows that our method outperforms the state-of-the-art methods.