Automatic　image　annotation　is　a　challenging　problem　in　image　understanding　areas.　The　existing　models　directly　extract　visual　features　from　segmented　image　regions.　Since　segmented　image　regions　may　still　have　multi-objects,　the　extractive　visual　features　may　not　effectively　describe　corresponding　regions.　In　order　to　overcome　the　above　problems,　an　image　annotation　model　based　on　multi-scale　salient　region　is　proposed.　In　this　model,　first,　each　image　is　segmented　by　using　multi-scale　grid-based　segmentation　method.　Second,　global　contrast-based　method　is　used　to　extract　the　saliency　maps　from　each　image　region.　Third,　visual　features　are　extracted　from　each　salient　region.　Finally,　multi-scale　visual　features　of　image　regions　are　fused　and　applied　to　automatic　image　annotation.　Our　model　can　improve　the　object　descriptions　of　images　and　image　regions.　Experimental　results　conducted　on　Corel　5K　datasets　verify　the　effectiveness　of　proposed　model.　©　2014　Springer　Science+Business　Media　New　York.