State-of-the-art　optimisation　methods　for　salient　object　detection　neglect　that　saliency　maps　of　different　images　usually　show　different　imperfections.　Therefore,　the　saliency　maps　of　some　images　cannot　achieve　effective　optimisation.　Based　on　the　observation　that　the　saliency　maps　of　semantically　similar　images　usually　show　similar　imperfections,　the　authors　propose　an　optimisation　method　for　salient　object　detection　based　on　semantic-aware　clustering　and　conditional　random　field　(CRF),　named　CCRF.　They　first　cluster　the　training　images　into　some　clusters　using　the　image　semantic　features　extracted　by　using　a　deep　convolutional　neural　network　model　for　image　classification.　Then　for　each　cluster,　they　use　a　CRF　to　optimise　the　saliency　maps　generated　by　existing　salient　object　detection　methods.　A　grid　search　method　is　used　to　compute　the　optimal　weights　of　the　kernels　of　the　CRF.　The　saliency　maps　of　the　testing　images　are　optimised　by　the　corresponding　CRFs　with　the　optimal　weights.　The　experimental　results　with　13　typical　salient　object　detection　methods　on　four　datasets　show　that　the　proposed　CCRF　algorithm　can　effectively　improve　the　results　of　a　variety　of　image　salient　object　detection　methods　and　outperforms　the　compared　optimisation　methods.　©　The　Institution　of　Engineering　and　Technology　2019