Image retrieval using asymmetric bagging and FSVM
基于非对称打包和FSVM的图像检索

Recently, SVMs(support vector machines) have been widely used in image retrieval as a method to improve the retrieval performance. However, conventional SVMs encounter four problems: small size of positive samples, asymmetry problem of training samples, over-fitting and weakly real-time. To solve these problems, an asymmetric bagging based fuzzy support vector machine (AB-FSVM) is proposed. An asymmetric bagging is made to negative samples, and then based on fuzzy theory and SVM, the retrieval images are gotten. Experimental results based on a set of Corel images show that the proposed system performs much better than the previous methods, especially when the size of positive samples is small.