Hybrid Method Using EDMS & Gabor for Shape and Texture

Abstract

The shape is essential to image formats where the textual image is the best example of the binary image representation. Feature extraction is a fundamental process in pattern recognition. the shape and texture recognition system consists of five major tasks which are involved pre-processing, segmentation, feature extraction, classification and recognition. GENERALLY, less discriminative features in global and local feature approach leads to reduce in recognition rate. By proposing a global and local approach that produces more discriminative features and less dimensionality of data, these problems are overcome. Two feature extraction methods are studied namely Gabor filter and edge direction matrix (EDMS) and combination of two popular feature extraction methods is proposed The proposed method is a combination of Gabor filter and EDMS method which applied to reduce the dimensionality of data. this collaboration aims to make use of the major advantages each one presents, by simultaneously complementing each other, in order to elevate their weak points. By using classifier approaches such as random forest, the proposed combinative descriptor is compared with the state of the art combinative methods based on Gray-Level Co-occurrence matrix and moment invariant on two benchmark dataset MPEG-7 CE-Shape-1, Enghlishfnt. The experiments have shown the superiority of the introduced descriptor over the GLCM moment invariants from the literature.