To　realize　effective　feature　representation,　this　paper　proposes　a　new　complex　networks　(CNs)-based　feature　fusion　scheme　to　recognize　texture　images.　Specifically,　we　propose　two　feature　extractors　to　detect　the　global　and　local　features　of　texture　images　respectively.　To　capture　the　global　features,　we　first　map　a　texture　image　as　an　undirected　graph　based　on　pixel　location　and　intensity,　and　three　feature　measurements　are　selected　to　further　decipher　the　image　features,　which　retains　as　much　image　information　as　possible.　Next,　given　the　original　band　images　(BIs)　and　the　generated　feature　images,　we　encode　them　using　local　binary　patterns　(LBPs).　Thus,　the　global　feature　vector　is　obtained　by　concatenating　four　spatial　histograms.　To　decipher　the　local　features,　we　jointly　transfer　and　fine-tune　a　pre-trained　VGGNet-16　model.　After　then,　we　fuse　the　middle　outputs　of　max-pooling　layers　(MPs)　and　generate　the　local　feature　vector　based　on　a　global　average　pooling　layer　(GAP).　Finally,　the　global　and　local　feature　vectors　are　connected　to　form　the　final　feature　representation　of　texture　images.　Experiment　results　show　that　the　proposed　scheme　outperforms　the　state-of-the-art　statistical　descriptors　and　several　deep　convolutional　neural　network　(CNN)　models.　©　2021　IEEE.