  We define the task of salient structure (SS) detection to unify the
saliency-related tasks like fixation prediction, salient object detection, and
other detection of structures of interest. In this study, we propose a unified
framework for SS detection by modeling the two-pathway-based guided search
strategy of biological vision. Firstly, context-based spatial prior (CBSP) is
extracted based on the layout of edges in the given scene along a fast visual
pathway, called non-selective pathway. This is a rough and non-selective
estimation of the locations where the potential SSs present. Secondly, another
flow of local feature extraction is executed in parallel along the selective
pathway. Finally, Bayesian inference is used to integrate local cues guided by
CBSP, and to predict the exact locations of SSs in the input scene. The
proposed model is invariant to size and features of objects. Experimental
results on four datasets (two fixation prediction datasets and two salient
object datasets) demonstrate that our system achieves competitive performance
for SS detection (i.e., both the tasks of fixation prediction and salient
object detection) comparing to the state-of-the-art methods.
