Saliency　estimation　becomes　a　hot　research　topic　due　to　its　wide　and　successful　application　in　almost　all　vision　related　problems.　However,　it　is　still　far　from　satisfactory　in　saliency　estimation　techniques　due　to　the　complex　visual　content　and　various　requirements.　In　this　paper,　we　propose　a　manifold　ranking　based　kernel　propagation　(MRKP)　approach　for　visual　saliency　estimation.　MRKP　begins　to　work　on　background　seeds　for　manifold　ranking　on　four　image　boundaries　individually　and　select　representative　salient　seeds.　Pairwise　constraints　of　must-link　and　cannot-link　are　formed　with　the　boundary　background　seeds　and　selected　salient　seeds.　Then,　pairwise　constraints　guided　saliency　seed　kernel　learning　and　saliency　kernel　propagation　are　sequentially　conducted　in　MRKP　to　estimate　visual　saliency　of　images.　Experimental　results　demonstrate　that　the　proposed　MRKP　has　a　good　ability　of　learning　discriminative　kernel　structure　for　saliency　estimation.