In　this　paper,　a　novel　and　effective　pornographic　image　recognition　method　is　proposed.　Contributions　of　this　paper　include　two　aspects.　(1)　Due　to　the　fact　that　the　images　are　mostly　stored　and　transmitted　with　JPEG　compressed　format　on　Internet,　feature　extraction　is　directly　performed　in　the　compressed　domain.　The　exacted　features　include　those　derived　from　skin　color　regions,　the　results　of　image　retrieval,　human　face　and　region　of　interest,　as　well　as　the　global　features　of　color　and　texture.　(2)　Data　mining　method　is　employed　to　search　for　the　potential　decision　rules　from　large-scale　image　feature　sets.　Taken　the　misclassification　cost　and　test　cost　into　account,　multi-cost　sensitive　decision　tree　is　constructed　first　to　improve　the　recognition　speed　and　accuracy.　Furthermore,　the　concept　of　pornography　degree　is　introduced　into　the　decision　rules,　which　is　output　as　the　recognition　results　to　represent　the　probability　of　the　image　being　judged　as　pornographic.　Experimental　results　show　that,　the　recognition　speed　of　the　proposed　method　is　almost　three　times　faster　than　the　classical　pixel　domain-based　recognition　method,　and　the　recognition　accuracy　is　also　higher　in　terms　of　True　Alarm　Rate　(TPR)　and　False　Alarm　Rate　(FPR).　(C)　2012　Elsevier　B.V.　All　rights　reserved.