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Shapes and texture image recognition usage is an essential branch of patternrecognition. It is made up of techniques that aim at extracting informationfrom images via human knowledge and works. Local Binary Pattern (LBP) ensuresencoding global and local information and scaling invariance by introducing alook-up table to reflect the uniformity structure of an object. However, edgedirection matrixes (EDMS) only apply global invariant descriptor which employsfirst and secondary order relationships. The main idea behind this methodologyis the need of improved recognition capabilities, a goal achieved by thecombinative use of these descriptors. This collaboration aims to make use ofthe major advantages each one presents, by simultaneously complementing eachother, in order to elevate their weak points. By using multiple classifierapproaches such as random forest and multi-layer perceptron neural network, theproposed combinative descriptor are compared with the state of the artcombinative methods based on Gray-Level Co-occurrence matrix (GLCM with EDMS),LBP and moment invariant on four benchmark dataset MPEG-7 CE-Shape-1, KTH-TIPSimage, Enghlishfnt and Arabic calligraphy . The experiments have shown thesuperiority of the introduced descriptor over the GLCM with EDMS, LBP andmoment invariants and other well-known descriptor such as Scale InvariantFeature Transform from the literature.

Edge direction matrixes-based local binar patterns descriptor for shape pattern recognition