基于图像到类(I2C)距离度量的图像分类是一种新颖的方法，但其分类性能仍有待提高． 为此，文中提出了一种基于 JointBoost I2C 距离度量的图像分类方法． 首先生成原型特征集，该集合中的样本具有代表性，故计算测试图像到该原型特征集的距离更有效;然后根据 JointBoost 算法的思想，联合多个 I2C 距离度量生成一个强分类器，并将空间信息融合到强分类器中． 实验结果表明，该方法在图像分类实验中具有更高的分类性能．

Image classification on the basis of image-to-class (I2C) distance metric is a novel method． However，its classification performance needs to be further improved． In this paper，a new image classification method on the basis of JointBoost I2C distance metric is proposed． In this method，a prototype feature set with representative sam-ples is generated,which makes the calculation of distance from the test image to the set more effective． Then，on the basis of JointBoost algorithm，multiple I2C distance metrics are combined to generate a strong classifier for in-tegrating spatial information． Experimental results show that the proposed method is of higher performance for image classification．