  Traditional saliency detection via Markov chain only considers boundaries
nodes. However, in addition to boundaries cues, background prior and foreground
prior cues play a complementary role to enhance saliency detection. In this
paper, we propose an absorbing Markov chain based saliency detection method
considering both boundary information and foreground prior cues. The proposed
approach combines both boundaries and foreground prior cues through
bidirectional Markov chain. Specifically, the image is first segmented into
superpixels and four boundaries nodes (duplicated as virtual nodes) are
selected. Subsequently, the absorption time upon transition node's random walk
to the absorbing state is calculated to obtain foreground possibility.
Simultaneously, foreground prior as the virtual absorbing nodes is used to
calculate the absorption time and obtain the background possibility. Finally,
two obtained results are fused to obtain the combined saliency map using cost
function for further optimization at multi-scale. Experimental results
demonstrate the outperformance of our proposed model on 4 benchmark datasets as
compared to 17 state-of-the-art methods.
