In　this　paper,　a　new　visual　saliency　detection　method　is　proposed　based　on　the　spatially　weighted　dissimilarity.　We　measured　the　saliency　by　integrating　three　elements　as　follows:　the　dissimilarities　between　image　patches,　which　were　evaluated　in　the　reduced　dimensional　space,　the　spatial　distance　between　image　patches　and　the　central　bias.　The　dissimilarities　were　inversely　weighted　based　on　the　corresponding　spatial　distance.　A　weighting　mechanism,　indicating　a　bias　for　human　fixations　to　the　center　of　the　image,　was　employed.　The　principal　component　analysis　(PCA)　was　the　dimension　reducing　method　used　in　our　system.　We　extracted　the　principal　components　(PCs)　by　sampling　the　patches　from　the　current　image.　Our　method　was　compared　with　four　saliency　detection　approaches　using　three　image　datasets.　Experimental　results　show　that　our　method　outperforms　current　state-of-the-art　methods　on　predicting　human　fixations.