To　overcome　the　limitations　of　original　local　binary　patterns　(LBPs),　this　article　proposes　a　new　texture　descriptor　aided　by　complex　networks　(CNs)　and　LBPs,　named　CN-LBP.　Specifically,　we　first　abstract　a　texture　image　(TI)　as　directed　graphs　over　different　bands　with　the　help　of　pixel　distance,　intensity,　and　gradient　(magnitude　and　angle).　Second,　several　CN　feature　measurements,　including　clustering　coefficient,　in-degree　centrality,　out-degree　centrality,　and　eigenvector　centrality),　are　selected　to　decipher　the　texture　features,　which　generates　four　feature　images　retaining　the　image　information　as　much　as　possible.　Third,　given　the　original　TIs,　gradient　images　(GIs),　and　generated　feature　images,　we　can　obtain　the　discriminative　representation　of　TIs　based　on　a　uniform　LBP　(ULBP).　Finally,　the　feature　vector　is　obtained　by　jointly　calculating　and　concatenating　spatial　histograms.　In　contrast　to　the　original　LBP,　the　proposed　texture　descriptor　contains　more　detailed　image　information　and　shows　resistance　to　imaging　and　noise.　Experiment　results　on　four　datasets　show　that　the　proposed　texture　descriptor　can　significantly　im　prove　the　classification　accuracies　compared　to　the　state-of-the-art　LBP-based　variants　and　deep　learning-based　methods.　©　2021　IEEE.