Image Classification Based on Multi-feature and Improved SVM Ensemble
Yan Fu, Xian Yan-ming · Jisuanji gongcheng · 2011
(Abstract )Aiming to the problem with poor classification accuracy of present image classification methods because they fail to apply fully complementary advantages between various single features of images and redundant information exists in the extracted features, this paper presents an image classification method based on multi-feature and improved Support Vector Machine(SVM) ensemble algorithm. Comprehensive features describing fully image content are extracted; redundant information is removed by transforming extracted features with Principal Component Analysis(PCA). RBaggSVM classifier is applied for classification. Simulation experimental result shows that this method has higher accuracy and faster speed of image classification than similar methods. (Key words ) ) ) )multi-feature; Principal Component Analysis(PCA); Support Vector Machine(SVM) ensemble; PCA-RBaggSVM algorithm; image classification