2DPCA-SIFT：一种有效的局部特征描述方法

颜雪军; 赵春霞; 袁夏

doi:10.3724/SP.J.1004.2014.00675

2DPCA-SIFT：一种有效的局部特征描述方法

doi: 10.3724/SP.J.1004.2014.00675

1.
南京理工大学计算机科学与工程学院南京 210094

基金项目:

国家自然科学基金（61272220），江苏省自然科学青年基金（BK2012399）资助

详细信息

作者简介:
赵春霞南京理工大学计算机科学与工程学院教授.主要研究方向为智能机器人技术，图像处理.E-mail：zhaochx@mail.njust.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2012-12-26
- 修回日期: 2013-03-04
- 刊出日期: 2014-04-20

2DPCA-SIFT：An Efficient Local Feature Descriptor

1.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094

Funds:

Supported by National Natural Science Foundation of China (61272220) and Natural Science Foundation of Jiangsu Province (BK2012399)

摘要

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.
- 2DPCA降维 /
- 局部特征描述 /
- 图像匹配 /
- 图像检索
Abstract: 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.
- 2DPCA dimension reduction /
- local descriptor /
- image matching /
- image retrieval

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参考文献(18)

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