In　the　paper,　we　present　an　interactive　image-segmentation　method　in　the　framework　of　graph　cut,　which　incorporates　not　only　traditional　color　and　gradient　constraints,　but　also　a　new　type　of　texture　constraint.　Given　an　image　with　user-input　strokes,　we　first　establish　the　color　and　texture　prior　models　of　the　foreground/background.　The　texture　prior　model,　which　is　key　to　establish　the　texture　constraints,　is　represented　by　local　binary　patterns　(LBP)　histograms.　Then,　an　energy　function　composed　of　color,　gradient,　and　texture　terms　is　formulated.　At　last,　by　using　graph　cut,　we　minimize　the　energy　function　to　obtain　the　foreground.　In　the　energy　function,　the　color　and　gradient　terms　have　similar　forms　with　traditional　methods.　The　texture　term　in　the　function　is　generated　using　a　proposed　randomized　texton-searching　algorithm.　First,　the　algorithm　locates　an　approximately　best　representative　texton　for　every　unknown　pixel　as　foreground　and　an　approximately　best　one　as　background,　through　randomized　searching.　Second,　it　computes　the　LBP　histograms　of　the　two　textons　as　the　pixel＇s　foreground/background　texture　descriptors,　respectively.　Finally,　the　distances　between　the　descriptors　and　the　foreground/background　prior　models　are　used　to　formulate　the　texture　term.　Experimental　results　demonstrate　that　our　method　outperforms　traditional　ones.　Copyright　(C)　2015　John　Wiley　&　Sons,　Ltd.