In　view　of　the　observation　that　saliency　maps　generated　by　saliency　detection　algorithms　usually　show　similarity　imperfection　against　the　ground　truth,　the　authors　propose　an　optimisation　algorithm　based　on　clustering　and　fitting　(CF)　for　saliency　detection.　The　algorithm　uses　a　fitting　model　to　represent　the　quantitative　relationship　between　ground　truth　and　algorithm-generated　saliency　maps.　The　authors　use　the　K-means　method　to　cluster　the　images　into　k　clusters　according　to　the　similarities　among　images.　Image　similarity　is　measured　in　terms　of　scene　and　colour　by　using　the　GIST　and　colour　histogram　features,　after　which　the　fitting　model　for　each　cluster　is　calculated.　The　saliency　map　of　a　new　image　is　optimised　by　using　one　of　the　fitting　models　which　correspond　to　the　cluster　to　which　the　image　belongs.　Experimental　results　show　that　their　CF-based　optimisation　algorithm　improves　the　performance　of　various　single　image　saliency　detection　algorithms.　Moreover,　the　improvement　achieved　by　their　algorithm　when　using　both　CF　strategies　is　greater　than　the　improvement　achieved　by　the　same　algorithm　when　not　using　the　clustering　strategy.　In　addition,　their　proposed　optimisation　algorithm　can　also　effectively　optimise　co-saliency　detection　algorithms　which　already　consider　multiple　similar　images　simultaneously　to　improve　saliency　of　single　images.