Numerous　stereoscopic　image　saliency　detection　algorithms　have　been　presented　to　detect　the　salient　objects　in　a　stereoscopic　image.　However,　they　typically　fail　to　uniformly　highlight　all　the　objects　when　the　image　contains　multiple　objects　or　complex　backgrounds.　In　this　paper,　we　propose　a　multi-cuedriven　optimization　(MCDO)　for　stereoscopic　image　saliency　detection.　MCDO　leverages　multiple　cues,　including　depth,　color,　and　spatial　position　to　optimize　the　saliency　maps　generated　by　existing　saliency　detection　algorithms.　Fully　connected　conditional　random　field　is　used　to　integrate　the　depth,　color,　and　spatial　cues　from　the　input　stereoscopic　image　to　ensure　that　pixels　with　similar　depth,　color,　and/or　spatial　position　have　similar　saliency　values.　Compared　with　original　saliency　maps,　the　optimized　saliency　maps　have　more　uniformly　highlighted　salient　objects,　whose　boundaries　are　more　precise,　and　fewer　incorrectly　detected　background　regions.　The　experimental　results　on　three　datasets　demonstrate　that　the　proposed　MCDO　method　can　effectively　improve　the　performance　of　stereoscopic　and　2-D　image　saliency　detection　algorithms.