Saliency　maps　computed　by　each　salient　object　detection　algorithm　show　similar　effect　against　ground　truth.　Therefore　this　paper　proposes　to　optimize　the　saliency　value　of　each　pixel　based　on　the　saliency　values　of　its　neighbouring　pixels.　It　proposes　a　machine　learning-based　method　to　learn　the　relationship　between　ground　truth　and　saliency　maps　and　among　saliency　values　of　neighbouring　pixels.　Then　it　applies　the　learned　models　to　a　new　saliency　map　and　then　confines　the　fitted　values　to　the　legitimate　range　to　obtain　the　optimized　saliency　map.　The　optimized　saliency　map　is　closer　to　the　ground　truth　and　better　than　the　original　saliency　map.　The　experimental　results　show　that　the　values　of　many　saliency　evaluation　metrics　improved　significantly　and　some　of　them　remained　unchanged.　They　illustrate　that　the　proposed　algorithm　is　suitable　for　optimizing　many　salient　object　detection　algorithms.　©　2017　IEEE.