  Automatic Salient object detection has received tremendous attention from
research community and has been an increasingly important tool in many computer
vision tasks. This paper proposes a novel bottom-up salient object detection
framework which considers both foreground and background cues. First, A series
of background and foreground seeds are selected from an image reliably, and
then used for calculation of saliency map separately. Next, a combination of
foreground and background saliency map is performed. Last, a refinement step
based on geodesic distance is utilized to enhance salient regions, thus
deriving the final saliency map. Particularly we provide a robust scheme for
seeds selection which contributes a lot to accuracy improvement in saliency
detection. Extensive experimental evaluations demonstrate the effectiveness of
our proposed method against other outstanding methods.
