A　dense　micro-block　difference　(DMD)-based　method　was　proposed　for　performing　texture　representation　that　is　a　fundamental　task　of　image　and　video　analysis.　However,　it　cannot　capture　effectively　the　rotation　invariance　and　multiscale　spatial　information　of　textures.　To　alleviate　these　problems,　in　this　paper,　we　propose　a　multiscale　symmetric　DMD　(MSDMD)　method　for　texture　classification.　In　particular,　we　first　combine　K-rotation　and　Gaussian　distribution　to　analyze　the　Symmetric　DMD　in　order　to　capture　the　rotation　invariance　of　textures.　Furthermore,　we　propose　a　high-order　vector　of　locally　aggregated　descriptor　called　HVLAD　by　incorporating　the　second-order　and　third-order　statistics　into　the　original　vector　of　VLAD.　To　effectively　extract　the　spatial　information　of　textures,　we　implement　the　above-mentioned　steps　in　a　Gaussian　pyramid　structure　to　construct　an　MSDMD　feature　and　use　a　support　vector　machine　(SVM)　to　perform　texture　classification.　The　experimental　results　on　five　available　published　texture　datasets　(KTH-TIPS,　CUReT,　UIUC,　UMD,　and　KTH-TIPS2-b)　reveal　that　our　proposed　method　is　effective　when　compared　with　15　representative　texture　classification　methods.