Abstract

Feature integration provides a computational
framework for saliency detection, and a lot of hand-crafted
integration rules have been developed. In this paper, we
present a principled extension, supervised feature integra-
tion, which learns a random forest regressor to discrimina-
tively integrate the saliency features for saliency computa-
tion. In addition to contrast features, we introduce regional
object-sensitive descriptors: the objectness descriptor char
acterizing the common spatial and appearance property of
the salient object, and the image-specic backgroundness
descriptor characterizing the appearance of the background
of a specic image, which are shown more important for
estimating the saliency. To the best of our knowledge, our
supervised feature integration framework is the rst success-
ful approach to perform the integration over the saliency
features for salient object detection, and outperforms the
integration approach over the saliency maps. Together with
fusing the multi-level regional saliency maps to impose
the spatial saliency consistency, our approach signicantly
outperforms state-of-the-art methods on seven benchmark
datasets. We also discuss several followup works which 

jointly learn the representation and the saliency map using
deep learning.