针对未知物体的分类问题，提出了一种基于支持向量机和关联成像的分类方法。该方法利用线性判别分析法提取出物体的特征向量，并根据该特征向量设计出应用于关联成像系统的特征散斑，将特征散斑照射物体获得桶探测器值，支持向量机可以依据桶探测器值进行判别从而获得物体的类别。该方法的可行性在MNIST数据集上得到了验证，结果表明，该方法在10个分类任务中均可取得较高的分类准确率，平均分类准确率达90.5%。与其他分类方法的对比结果表明，所提方法在准确率上更具优势。

A classification method based on support vector machine and correlation imaging is proposed to address the problem of unknown object recognition. The method utilizes linear discriminant analysis to extract feature vectors from the objects. Based on these feature vectors, the characteristic speckle patterns are designed and applied to a correlation imaging system. By illuminating the objects with the characteristic speckle patterns, the bucket detector values are obtained from the correlation imaging system. The support vector machine is then employed to discriminate and classify the objects based on these bucket detector values. The feasibility of this approach is validated on the MNIST dataset. The results demonstrate that high classification accuracies can be achieved by the proposed method in all ten classification tasks, with an average classification accuracy of 90.5%. The comparison results with other classification methods indicate that the proposed method has more advantages in accuracy.