现有的基于图像局部特征的目标识别算法，在保证较高识别率的情况下无法满足实时性要求。针对这个问题，考虑到多数局部特征是不稳定、不可靠或与目标无关的，可通过正确匹配的训练图像，对图像局部特征选取一个子集用于目标识别。提出一种在特征包方法基础上，通过无监督地选取鲁棒性强及足够特殊、稳定的局部特征用于目标识别的新方法并应用于目标识别实验。实验结果证实该方法在仅仅使用原图像约4%的局部特征的情况下获得了与使用全部局部特征几乎相近的目标识别率，目标识别时间由秒缩短至几十毫秒，满足了目标识别实时性要求。

Existing methods based on local features cannot recognize objects in real-time while keeping a high recognition rate.Considering that many local features are unstable，unreliable，or irrelevant，we are able to select a small subset of features used for recognition by correctly matching features in training images.A new，robust，and stable method based on a bag-of-features is proposed in this paper.Distinctive features are selected by an unsupervised preprocessing step.Our experiments demonstrate that this selection approach can reduce the amount of local features and reduce the memory requirements，while allowing an average of 4%of the original features per image to provide matching performance that is as accurate as the full set.The method can meet real-time requirements since the time required for matching has been reduced from seconds to tens of milliseconds.