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　can　not　capture　effectively　the　rotation　invariance　and　multiscale　spatial　information　of　textures.　To　alleviate　these　problems,　in　this　paper　we　propose　a　Multiscale　Symmetric　Dense　Microblock　Difference　(MSDMD)　method　for　texture　classification.　In　particular,　we　first　combine　K-rotation　and　Gaussian　distribution　to　construct　a　Symmetric　Dense　Micro-block　Difference　in　order　to　capture　the　rotation　invariance　of　textures.　Furthermore,　we　propose　a　High-order　Vector　of　Locally　Aggregated　Descriptor　called　HVLAD　by　incorporating　second-order　and　third-order　statistics　into　the　original　Vector　of　Locally　Aggregated　Descriptors　(VLAD).　To　effectively　extract　the　spatial　information　of　textures,　we　implement　the　above　steps　in　a　Gaussian　pyramid　structure　to　construct　a　MSDMD　feature,　and　use　a　Support　Vector　Machine　(SVM)　to　perform　texture　classification.　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　fifteen　representative　texture　classification　methods.　IEEE