  Salient object detection plays an important part in a vision system to detect
important regions. Convolutional neural network (CNN) based methods directly
train their models with large-scale datasets, but what is the crucial feature
for saliency is still a problem. In this paper, we establish a novel bottom-up
feature named convex hull overlap (CHO), combining with appearance contrast
features, to detect salient objects. CHO feature is a kind of enhanced Gestalt
cue. Psychologists believe that surroundedness reflects objects overlap
relationship. An object which is on the top of the others is attractive. Our
method significantly differs from other earlier works in (1) We set up a
hand-crafted feature to detect salient object that our model does not need to
be trained by large-scale datasets; (2) Previous works only focus on appearance
features, while our CHO feature makes up the gap between the spatial object
covering and the object's saliency. Our experiments on a large number of public
datasets have obtained very positive results.
