In　this　paper,　we　propose　visual　saliency　estimation　using　constraints.　Based　on　the　observations　that　salient　regions　are　generally　distinctive　from　the　background,　we　define　visual　saliency　as　the　possibility　of　being　assigned　to　the　label　of　the　most　salient　region.　First,　we　generate　an　initial　saliency　map　for　a　given　image　at　the　superpixel　level　using　superpixel　segmentation　and　three　common　priors.　Then,　we　select　salient　and　non-salient　seeds　from　the　initial　saliency　map　to　generate　adaptive　constraints.　Adaptive　constraints　are　able　to　propagate　the　seed　information　adaptively　by　their　correlations.　Finally,　we　produce　the　visual　saliency　map　by　propagating　saliency　seeds　to　the　whole　image　using　a　learned　non-linear　kernel　mapping.　Experimental　results　demonstrate　that　kernel　learning　and　seed　propagation　are　capable　of　learning　distinctive　saliency　from　images.　(c)　2018　Elsevier　B.V.　All　rights　reserved.