Most　pornographic　image　recognition　researches　focus　on　detection　accuracy.　However,　as　the　highly　increasing　of　web　data,　detection　speed　becomes　a　new　consideration.　In　this　paper,　the　new　issue　is　discussed　from　the　following　two　aspects:　1)　feature　extraction　in　compressed　domain　and　2)　classifier　design,　and　then　a　simple,　novel　and　yet　effective　pornographic　image　recognition　method　in　compressed　domain　is　proposed,　which　is　based　on　multi-cost　sensitive　decision　tree.　More　specifically,　some　features,　including:　features　based　on　skin　color　region,　features　based　on　the　results　of　image　retrieval,　features　based　on　face　and　regions　of　interesting　as　well　as　global　texture　and　color　features,　are　extracted　from　the　compressed　image　firstly.　Afterward,　a　multi-cost　sensitive　decision　tree　construction　algorithm　is　presented,　based　on　which　the　decision　tree　of　pornographic　image　recognition　is　established.　Experimental　results　show　the　proposed　method　can　not　only　effectively　improve　the　detection　accuracy　but　also　the　detection　speed.