Image Texture Feature Extraction & Recognition of Chinese Herbal Medicine Based on Gray Level Co-Occurrence Matrix

Abstract

In order to effectively extract Chinese herbal medicine (CHM) image feature information, and automatically identify the CHM images, a method of CHM image feature extraction and recognition based on gray level co-occurrence matrix (GLCM) is put forward. Firstly, on the basis of the acquired colour CHM image is converted to the gray-scale image, the four texture feature parameters, angular second moment (ASM)，inertia moment (IM)，entropy and correlation are extracted utilizing the GLCM, and then CHM image recognition is carried out by using those feature values with resistance geometric distortion. The experimental results show that the method of generating GLCM and extraction of image texture features can effectively identify the CHM image, which can bring significance to the modern recognition and identification of CHM.