Content-Based Xinjiang Folk Art Patterns Classification Using Fractal Dimension and SVM
To elucidate how to optimize combination features and to design a classifier with high classification accuracy, a challenging problem, a method based on error rate of classification as standard to select combined feature was presented so as to raise the classification accuracy. First, four kinds of fractal dimensions are extracted as texture features. Then, various combination features are training samples of SVM. With combination feature with the lowest classification error rate as a vector to be applied to the classification, the classification accuracy of the classifier can be improved. A variety of patterns are generated by primitive gene and regenerative gene. The proposed method is simple and easy in operation that can be widely popularized. So it can lay the foundation for the combination of image features.
fraetal dimensionXinjiang folk art patternssupport vector machine (SVM)image classification
国家科技期刊平台
NSTL
万方数据
维普
