An Image Classification Algorithm Based on SVM

Abstract

Image classification is a image processing method which to distinguish between different categories of objectives according to the different features of images. It is widely used in pattern recognition and computer vision. Support Vector Machine (SVM) is a new machine learning method base on statistical learning theory, it has a rigorous mathematical foundation, builts on the structural risk minimization criterion. We design an image classification algorithm based on SVM in this paper, use Gabor wavelet transformation to extract the image feature, use Principal Component Analysis (PCA) to reduce the dimension of feature matrix. We use orange images and LIBSVM software package in our experiments, select RBF as kernel function. The experimetal results demonstrate that the classification accuracy rate of our algorithm beyond 95%.