在图像检索的相关反馈中，引入支持向量机分类方法虽可以提升图像的检索性能，但是传统的支持向量机存在正样本数少、样本非对称、过学习和弱实时性的局限。针对上述问题，提出了一种基于非对称打包的FSVM算法。该算法首先对负样本进行非对称打包处理，最后结合模糊理论与SVM实现图像检索。Corel图片集上的实验表明，当正样本数较小时，该新算法的平均查准率-查全率要优于已有算法。

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.