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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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)

[1]	Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
[2]	Lourenço M, Barreto J P A, Vasconcelos F. sRD-SIFT: keypoint detection and matching in images with radial distortion. IEEE Transactions on Robotics, 2012, 28(3): 752-760
[3]	Lin Hai-Feng, Ma Yu-Feng, Song Tao. Research on object tracking algorithm based on SIFT. Acta Automatica Sinica, 2010, 36(8): 1204-1208(蔺海峰, 马宇峰, 宋涛. 基于SIFT 特征目标跟踪算法研究. 自动化学报, 2010, 36(8): 1204-1208)
[4]	Rublee E, Rabaud V, Konolige K G, Bradski J R. ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 2564-2571
[5]	Tian Q, Zhang S L, Zhou W G, Ji R R, Ni B B, Sebe N. Building descriptive and discriminative visual codebook for large-scale image applications. Multimedia Tools and Applications, 2011, 51(2): 441-477
[6]	Mikolajczyk K I, Schmid C. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630
[7]	Zeng Luan, Gu Da-Long. A SIFT feature descriptor based on sector area partitioning. Acta Automatica Sinica, 2012, 38(9): 1513-1519(曾峦, 顾大龙. 一种基于扇形区域分割的SIFT 特征描述符. 自动化学报, 2012, 38(9): 1513-1519)
[8]	Zeng Hui, Mu Zhi-Chun, Wang Xiu-Qing. A robust method for local image feature region description. Acta Automatica Sinica, 2011, 37(6): 658-664(曾慧, 穆志纯, 王秀青. 一种鲁棒的图像局部特征区域的描述方法. 自动化学报, 2011, 37(6): 658-664)
[9]	Bay H, Tuytelaars T, van Gool L. SURF: speeded up robust features. In: Proceedings of the 9th European Conference on Computer Vision. Graz, Austria: Springer, 2006. 404-417
[10]	Juan L, Gwun O. A comparison of SIFT, PCA-SIFT and SURF. International Journal of Image Processing, 2009, 3(4): 143-152
[11]	Huang C R, Chen C R, Chung P C. Contrast context histogram: an efficient discriminating local descriptor for object recognition and image matching. Pattern Recognition, 2008, 41(10): 3071-3077
[12]	Liu Ping-Ping, Zhao Hong-Wei, Zang Xue-Bai, Dai Jin-Bo. A fast local feature description algorithm. Acta Automatica Sinica, 2010, 36(1): 40-45(刘萍萍, 赵宏伟, 臧雪柏, 戴金波. 一种快速局部特征描述算法. 自动化学报, 2010, 36(1): 40-45)
[13]	Yang Heng, Wang Qing. A novel local invariant feature detection and description algorithm. Chinese Journal of Computers, 2010, 33(5): 935-944(杨恒, 王庆. 一种新的局部不变特征检测和描述算法. 计算机学报, 2010, 33(5): 935-944)
[14]	Ke Y, Sukthankar R. PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE, 2004. 506-513
[15]	Winder S, Hua G, Brown, M. Picking the best DAISY. In: Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 2009. 20-25
[16]	Shan H H, Cottrell G W. Looking around the backyard helps to recognize faces and digits. In: Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Anchorage, USA: IEEE, 2008. 1-8
[17]	Yang J, Zhang D Q, Frangi A F, Yang J Y. Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137
[18]	Yang J, Xu Y, Yang J Y. Bi-2DPCA: A fast face coding method for recognition. Pattern Recognition Recent Advances. Vienna, Austria: InTech, 2010. 313-340