Recently　bag-of-words　(BoW)　model　having　been　widely　used　in　textual　information　processing　has　been　extended　into　many　tasks　in　visual　domain　such　as　image　classification,　scene　analysis,　image　annotation　and　image　retrieval,　namely　bag-of-visual-words　(BoVW)　model.　Therefore,　it　is　essential　to　create　an　effective　visual　vocabulary.　Most　of　existing　approaches　create　visual　vocabularies　from　image　in　pixel　domain,　which　requires　extra　processing　time　for　decompressed　images,　since　most　images　are　stored　in　compressed　format.　In　this　paper　we　propose　to　create　a　visual　vocabulary　based　on　Scale　Invariant　Feature　Transform　(SIFT)　descriptor　in　compressed　domain　with　the　following　three　steps,　(1)　constructing　low-resolution　images　in　compressed　domain;　(2)　extracting　SIFT　descriptor　from　low-resolution　images;　and　(3)　creating　a　visual　vocabulary　based　on　extracted　SIFT　descriptors.　In　order　to　evaluate　the　performance　of　the　visual　words,　experiments　have　been　conducted　on　identifying　pornographic　images.　Experimental　results　indicate　that　the　proposed　method　can　recognize　pornographic　images　accurately　with　much　reduced　computational　time.　©　2011　IEEE.