Multi-features fusion classification method for texture image

Abstract

Aiming at the problem of poor classification and recognition rate for distorted texture image based on single texture feature, a classification method of texture image based on multi-features fusion is proposed. First, the corresponding GLCM features, HOG features, and HU moment features were extracted from the segmented texture images. Then, the three feature matrices were cascaded into a new feature matrix, and the principal component analysis method was used to reduce the dimension of the new feature matrix. Finally, the fused feature matrix was inputted to the support vector machine (SVM) for training, so that the final discriminant model was obtained. The model is applied to the classification of distorted texture images and compared with the single texture feature classification method through experiment. The results show that the multi-features fusion classification method improves the classification accuracy of distorted texture images and has better real-time performance.

Keywords

• support vector machines
• principal component analysis
• feature extraction
• image fusion
• image segmentation
• matrix algebra
• image texture
• image classification
• multifeatures fusion classification method
• classification accuracy
• distorted texture image
• hog features
• hu moment features
• segmented texture images
• feature matrices
• principal component analysis method
• fused feature matrix
• single texture feature classification method
• glcm features