In　the　paper,　we　present　a　method　of　interactive　image　segmentation　with　texture　constraints　in　the　framework　of　graph　cut.　Given　an　image,　we　first　gather　user-marked　information　to　establish　the　color　and　texture　prior　models　of　the　foreground　and　background.　Then,　we　formulate　an　energy　function　composed　of　color,　gradient　and　texture　terms.　At　last,　the　foreground　is　extracted　by　minimizing　the　energy　function　using　graph　cut.　In　the　energy　function,　the　texture　term　describes　the　difference　between　the　texture　prior　models　and　the　texture　descriptors　of　each　pixel　to　be　labeled.　The　foreground/background　texture　prior　model　is　represented　as　histograms　of　Local　Binary　Patterns　(LBP).　Every　pixel　to　be　labeled　in　the　image　has　a　foreground　and　a　background　texture　descriptor,　which　are　obtained　by　a　randomized　texton-searching　algorithm.　The　newly　added　texture　term　is　effective　to　overcome　the　difficulty　in　locating　real　boundaries　when　dealing　with　textured　foreground/background.　Experimental　results　demonstrate　that,　with　the　same　amount　of　user　interaction,　our　method　generates　better　results　than　traditional　ones.　Copyright　©　ACM.