In　this　paper,　firstly,　an　adaptive　Dense-SIFT　feature　extraction　method　is　proposed,　which　can　adaptively　adjust　the　size　of　local　window　using　the　edge　information　of　image.　Next,　a　large　scale　image　retrieval　method　is　proposed.　The　adaptive　Dense-SIFT　features　are　extracted　from　the　database　images.　Bag　of　Word　(BoW)　model　is　then　adopted　to　create　the　corresponding　histograms　of　visual　words　frequency　to　represent　the　features.　To　efficiently　describe　the　image　content,　the　feature　vectors　are　constructed　by　combining　the　visual　words　histograms　of　Dense-SIFT　feature　with　the　72-dimensional　HSV　(Hue,　Saturation,　Value)　color　feature.　In　retrieval　process,　the　top-h　most　similar　images　are　returned　by　computing　the　similarity　between　the　feature　vectors　of　querying　image　and　those　of　the　images　in　database.　Finally,　to　further　improve　the　accuracy,　the　returned　images　are　re-ranked　with　context　similarity　information.　The　experimental　results　on　Corel-5K　and　Oxford　Buildings　dataset　show　that　the　proposed　method　outperforms　the　existing　image　retrieval　methods.