To　improve　the　performance　of　sketch-based　image　retrieval　(SBIR)　methods,　most　existing　SBIR　methods　develop　brand　new　SBIR　methods.　In　fact,　a　re-ranking　approach,　which　can　refine　the　retrieval　results　of　SBIR　methods,　is　also　beneficial.　Inspired　by　this,　in　this　paper,　an　SBIR　re-ranking　approach　based　on　multi-clustering　is　proposed.　In　order　to　make　the　re-ranking　approach　invisible　to　users　and　adaptive　to　different　types　of　image　datasets,　we　made　it　an　unsupervised　method　using　blind　feedback.　Distinguished　from　the　existing　methods,　this　re-ranking　approach　uses　the　semantic　information　of　three　types　of　images:　edge　maps,　object　images　(images　with　black　background　and　natural　images＇　foreground　objects)　and　natural　images　themselves.　With　the　initial　retrieval　results　of　an　SBIR　method,　our　approach　first　does　the　clustering　operation　for　three　types　of　images.　Then,　we　utilize　the　clustering　results　to　generate　a　cluster　score　for　each　initial　retrieval　result.　Finally,　the　cluster　score　is　used　to　calculate　the　final　retrieval　scores　for　the　initial　retrieval　results.　The　experiments　on　different　SBIR　datasets　are　conducted.　Experimental　results　demonstrate　that,　by　implementing　our　re-ranking　approach,　the　retrieval　accuracy　of　a　variety　of　SBIR　methods　is　increased.　Furthermore,　the　comparisons　between　our　re-ranking　method　and　the　existing　re-ranking　methods　are　given.　©　1991-2012　IEEE.