Existing salient object extraction methods for the low depth-of-field (DOF) image are usually based on local saliency. However, in the low DOF image, the smooth region of salient objects is similar to the background in local saliency, so they are easily confused. In this paper, a novel salient object extraction method is proposed by introducing Support Vector Data Description (SVDD) for salient object shape description. It is the first time that SVDD is used for salient object extraction. SVDD makes full use of global characteristics of salient objects, which makes it possible for our approach to accurately extract salient objects containing smooth regions. Experiments on a Flickr dataset consisting of 141 low DOF images indicate that F-measure of our approach is better than the existing methods.