SIFT Feature Matching Algorithm Based on Vector Angle
WU Wei-Jiao,WANG Min,HUANG Xin-Han,MAO Shang-Qin
Key Laboratory of Image Processing and Intelligent Control of Ministry of Education,Huazhong University of Science Technology,Wuhan 430074 Department of Control Science and Engineering,Huazhong University of Science Technology,Wuhan 430074
Abstract An approximate nearest neighbor search method based on vector angle is proposed. Firstly,the vector angles between high dimensional vectors and a stochastic selected reference vector are computed,and these angles are sorted. Then,the angle of reference vector and the query vector is computed,and the angle is found in the sorted angles by binary search algorithm. Finally,taking the angle as the center,the approximate nearest neighbor of the query vector is searched in the setting range. The experimental results show that the scale invariant feature transform feature matching can be accelerated significantly without undermining the performance of feature matching.
[1] Krystian M,Cordelia S. A Performance Evaluation of Local Descriptors. IEEE Trans on Pattern Analysis and Machine Intelligence,2005,27(10): 1615-1630 [2] Herbert B,Andreas E,Tinne T,et al. SURF: Speeded up Robust Features // Proc of the 9th European Conference on Computer Vision. Graz,Austria,2006: 404-417 [3] Yan K,Sukthankar R. PCA-SIFT: A More Distinctive Representation for Local Image Descriptors // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington,USA,2004: 506-513 [4] Faraj A,Danijela R D,Axel G. VF-SIFT: Very Fast SIFT Feature Matching // Proc of the 32nd DAGM Symposium. Darmstadt,Germany,2010: 222-231 [5] Gionis A,Indyk P,Motwani R. Similarity Search in High Dimensions via Hashing // Proc of the 25th International Conference on Very Large Data Bases. San Francisco,USA,1999: 518-529 [6] Auclair A,Cohen L D,Vincent N. How to Use SIFT Vectors to Analyze an Image with Database Templates // Proc of the 5th International Workshop on Adaptive Multimedia Retrieval. Paris,France,2008: 224-236 [7] He Zhoucan,Wang Qin,Yang Heng. An Extended Local Sensitivity Hash Algorithm for Efficient Image Matching. Journal of Sichuan University: Natural Science Edition,2010,47(2): 269-274(in Chinese) (何周灿,王 庆,杨 恒.一种面向快速图像匹配的扩展LSH算法.四川大学学报:自然科学版,2010,47(2): 269-274) [8] LoweD G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision,2004,60(2): 91-110
2013, Vol. 26 
