Study on image classification based on SVM and the fusion of multiple features

In this paper, an adaptive feature-weight adjusted image classification method is proposed, which is based on the SVM and the fusion of multiple features. Firstly, classifier was separately constructed for each image feature, then automatically learn the weight coefficient of each feature by training data set and the classifiers constructed. At last, a complexity classifier is created by combining the separate classifier and the corresponding weight coefficient. The experiment result showed that our scheme improved the performance of image classification and had adaptive ability comparing with general approach. Moreover, the scheme has certain robustness because of avoiding the impact brought by various dimension of each feature.