Salient　object　segmentation　is　well　known　for　detecting　and　segmenting　objects　using　saliency　map　as　input.　In　this　paper,　we　propose　a　salient　object　segmentation　method　which　integrates　saliency,　superpixel,　and　background　connectivity　prior.　First,　our　method　extracts　the　superpixels　of　an　image　by　the　simple　linear　iterative　clustering　algorithm.　Second,　based　on　superpixel　representation,　we　use　background　connectivity　prior　to　characterize　the　spatial　layout　of　each　superpixel　in　a　color　space　with　respect　to　the　image　boundary.　Third,　considering　both　saliency　and　background　connectivity　values,　we　label　four　kinds　of　superpixel-level　seeds　and　feed　them　to　superpixel-level　GrabCut　method.　Because　superpixel　representation　generates　only　a　few　seeds,　the　optimization　of　GrabCut　method　converges　fast.　Finally,　for　further　improvement,　we　crop　a　rectangular　region　which　contains　the　segmented　object　obtained　in　the　third　step　and　apply　GrabCut　at　the　pixel　level　to　produce　the　final　segmentation　result.　Experimental　results　on　eight　typical　datasets　demonstrate　that　in　terms　of　both　performance　and　computational　efficiency,　the　proposed　segmentation　method　outperforms　existing　state-of-the-art　methods.