In　this　paper,　we　propose　a　data-driven　approach　to　detect　the　probabilistic　salient　object　contour,　which　is　formulated　as　predicting　the　probability　of　superpixel　boundaries　being　on　the　object　contour　based　on　the　learned　regressor.　Each　superpixel　boundary　is　jointly　described　by　the　superpixel　saliency,　superpixel　contrast,　and　boundary　geometry　features.　Experimental　results　on　the　benchmark　data　set　validate　the　effectiveness　of　our　approach.　Furthermore,　we　demonstrate　that　the　predicted　probabilistic　salient　object　contour　is　useful　for　improving　the　multiple　segmentations　for　salient　object　detection.