Infrared　ship　segmentation　is　extensively　applied　in　military　fields.　Due　to　noise　and　intensity　inhomogeneity,　the　segmentation　of　infrared　ship　is　a　challenging　task.　The　fuzzy　c-means　(FCM)　clustering　algorithm　is　widely　used　in　image　segmentation.　However,　traditional　FCM　is　sensitive　to　noise　and　unable　to　obtain　desirable　segmentation　results　for　infrared　ship　images.　In　this　article,　a　novel　probability　induced　intuitionistic　FCM　clustering　algorithm　is　proposed　to　address　the　problem.　First,　the　target　probability　information　is　incorporated　into　intuitionistic　FCM　to　induce　and　refine　membership　which　is　affected　by　interferences.　Second,　by　making　use　of　neighborhood　information　in　the　form　of　a　regularization　term,　the　proposed　method　could　suppress　intensity　inhomogeneity　as　well　as　maintain　image　details.　Experimental　results　demonstrate　that　the　proposed　method　could　achieve　better　results　than　12　other　comparing　algorithms　for　infrared　ship　segmentation.