Locations　of　images　have　been　widely　used　in　many　application　scenarios　for　large　geotagged　image　corpora.　As　to　images　that　are　not　geographically　tagged,　we　estimate　their　locations　with　the　help　of　the　large　geotagged　image　set　by　content-based　image　retrieval.　Bag-of-words　image　representation　has　been　utilized　widely.　However,　the　individual　visual　word-based　image　retrieval　approach　is　not　effective　in　expressing　the　salient　relationships　of　image　region.　In　this　paper,　we　present　an　image　location　estimation　approach　by　multisaliency　enhancement.　We　first　extract　region-of-interests　(ROIs)　by　mean-shift　clustering　on　the　visual　words　and　salient　map　of　the　image　based　on　which　we　further　determine　the　importance　of　the　ROI.　Then,　we　describe　each　ROI　by　the　spatial　descriptors　of　visualwords.　Finally,　regionbased　visual　phrases　are　generated　to　further　enhance　the　saliency　in　image　location　estimation.　Experiments　show　the　effectiveness　of　our　proposed　approach.