Bag-of-words　(BoW)　model　has　been　widely　used　in　pornographic　images　recognition　and　filtering.　Most　of　existing　methods　create　BoW　from　images　with　a　scale-invariant　feature　transform　(SIFT)　descriptor　in　the　pixel　domain.　These　methods　require　extra　processing　time　to　decompress　images　in　compressed　formats.　In　addition,　the　SIFT　descriptor　only　views　local　feature　points　in　centers　of　some　regions　as　BoW,　which　ignores　a　major　role　of　image　region　in　the　human　visual　system.　Different　from　the　above　methods　in　this　paper,　a　BoW　approach　based　on　the　visual　attention　model　is　proposed　to　recognize　pornographic　images　in　compressed　domain,　which　includes　the　following　steps:　(1)　face　is　detected　to　remove　the　face　or　ID　photo　from　some　benign　images;　(2)　a　visual　attention　model　is　built　according　to　the　characteristics　of　pornographic　image;　(3)　pornographic　regions　are　detected　by　visual　attention　model　in　compressed　domain;　(4)　four　features　of　color,　texture,　intensity　and　skin　are　extracted　in　pornographic　regions;　(5)　BoW　is　created　by　k-means　cluster　and　(6)　BoW　will　be　used　to　represent　and　recognize　pornographic　images.　Experimental　results　show　that　proposed　BoW　approach　based　on　the　visual　attention　model　can　more　accurately　recognize　pornographic　images　with　less　computational　time.　(C)　2013　Elsevier　B.V.　All　rights　reserved.