中文摘要：

PCA-SIFT（Principal component analysis-scale invariant feature transform）方法通过对归一化梯度向量进行PCA降维，在保留特征不变性的同时，有效地降低了特征矢量的维数，从而提高了局部特征的匹配速度．但PCA-SIFT中对本征向量空间的求解非常耗时，极大地限制了PCA-SIFT的灵活性与应用范围．本文提出采用2DPCA对梯度向量块进行降维的特征描述方法．该方法相比于PCA-SIFT，可以快速地求解本征空间．实验结果表明：2DPCA。SIFT在多种图像变换匹配和图像检索实验中可以实现与PCA-SIFT相当的性能，并且从计算效率上看，2DPCA-SIFT具有更好的扩展性．

英文摘要：

Principal component analysis - scale invariant feature transform （PCA-SIFT） applies principal components analysis （PCA） to the normalized gradient vector. It effectively reduces the dimension of feature representation and improves the matching speed while maintaining the descriptor＇s invariance. However, PCA-SIFT needs an additional step of eigenspace computation which is time-consuming. This step greatly limits the flexibility and applications of PCA-SIFT. In this paper, we adopt the 2DPCA to reduce the descriptor＇s dimension and build the descriptors. Compared to the PCA-SIFT, this method can finish the eigenspace calculation in real time. The experiments show that the proposed method can get competitive performance when compared to PCA-SIFT in different image matching and image retrieval applications, and can be easier to be expanded for its good computational efficiency.