  We propose a novel method for salient object detection in different images.
Our method integrates spatial features for efficient and robust representation
to capture meaningful information about the salient objects. We then train a
conditional random field (CRF) using the integrated features. The trained CRF
model is then used to detect salient objects during the online testing stage.
We perform experiments on two standard datasets and compare the performance of
our method with different reference methods. Our experiments show that our
method outperforms the compared methods in terms of precision, recall, and
F-Measure.
