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2DPCA-SIFT:一种有效的局部特征描述方法

DOI: 10.3724/SP.J.1004.2014.00675, PP. 675-682

Keywords: 2DPCA降维,局部特征描述,图像匹配,图像检索

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Abstract:

?PCA-SIFT(Principalcomponentanalysis—scaleinvariantfeaturetransform)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.

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