[1]TANG Kun,HAN Bin.A SIFT matching algorithm based on the distance to reference point[J].CAAI Transactions on Intelligent Systems,2015,10(3):376-380.[doi:10.3969/j.issn.1673-4785.201311020]
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A SIFT matching algorithm based on the distance to reference point

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 3 Page number: 376-380 Column: 学术论文—机器学习 Public date: 2015-06-25
Title:
A SIFT matching algorithm based on the distance to reference point
Author(s):
TANG Kun1;  HAN Bin2
1. School of Transportation, Southeast University, Nanjing 210096, China;
2. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang 212000, China
Keywords:
scale invariant feature transform (SIFT); distance to reference point (DRP) algorithm; feature point matching; nearest neighbor; reference point
CLC:
TP319
DOI:
10.3969/j.issn.1673-4785.201311020
Abstract:
To address the high time cost of feature point matching in scale invariant feature transform (SIFT), a new SIFT feature point matching algorithm based on the distance to reference point—DRP algorithm is put forward. Firstly, distances from the reference point to every feature point to be matched is computed using DRP algorithm. Then, these distances computed previously is ordered and saved in a dataset named as distance of ordering. Next, distances from the reference point to the feature point to be queried is also computed. After that, the nearest neighbor of the distance in distance of ordering is retrieved with binary search and returned as index of center. Finally, the nearest neighbor of feature point to be queried is searched one by one in a certain range whose center is index of center. It is proven by experiment tested on ACF (affine covariant features) pictures from VGG(visual geometry group) laboratory that DRP algorithm can effectively decrease the time cost of SIFT feature points matching without loss of matching results compared with the classical SIFT algorithm.
References:
[1] LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
[2] 吴伟交, 王敏, 黄心汉, 等. 基于向量夹角的SIFT特征点匹配算法[J]. 模式识别与人工智能, 2013, 26(1): 123-127.WU Weijiao, WANG Min, HUANG Xinhan, et al. SIFT feature matching algorithm based on vector angle. Pattern Recognition and Artificial Intelligence, 2013, 26(1): 123-127.
[3] KRYSTIAN M, CORDELIA S. A performance evaluation of local descriptors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630.
[4] GIONIS A, INDYK P, MOTWANI R. Similarity search in high dimensions via hashing[C]//Proceedings of the 25th International Conference on Very Large Data Bases. San Francisco, USA, 1999: 518-529.
[5] BEIS J S, LOWE D G. Shape indexing using approximate nearest-neighbor search in high-dimensional spaces[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. San Juan, Puerto Rico, 1997: 1000-1006.
[6] FERHATOSMANOGLU H, TUNCEL E, AGRAWAL D, et al. Vector approximation based indexing for non-uniform high dimensional data sets[C]//Proceedings of the International Conference on Information and Knowledge Management. McLean, USA, 2000: 202-209.
[7] WEBER R, SCHEK H J, BLOTT S. A quantitative analysis and performance study for similarity-search methods in high dimensional spaces[C]//Proceedings of the International Conference on Very Large Databases. New York, 1998: 194-205.
[8] JAGADISH H V, OOI B C, TAN K L, et al. IDistance: an adaptive B+-tree based indexing method for nearest neighbor search[J]. ACM Transactions on Database Systems, 2005, 30(2): 364-398.
[9] AUCLAIR A, COHEN L D, VINCENT N. How to use SIFT vectors to analyze an image with database templates[C]//Proceedings of the 5th International Workshop on Adaptive Multimedia Retrieval. Paris, France, 2008: 224-236.
[10] SLANEY M, CASEY M. Locality-sensitive hashing for finding nearest neighbors[J]. IEEE Signal Processing Magazine, 2008, 25(2): 128-131.
[11] 张军旗, 周向东, 王梅, 等. 基于聚类分解的高维度量空间索引B+-tree[J]. 软件学报, 2008, 19(6): 1401-1412.ZHANG Junqi, ZHOU Xiangdong, WANG Mei, et al. Cluster splitting based high dimensional metric space index B+-tree[J]. Journal of Software, 2008, 19(6): 1401-1412.
[12] 杨恒, 王庆, 何周灿. 面向高维图像特征匹配的多次随机子向量量化哈希算法[J]. 计算机辅助设计与图形学学报, 2010, 22(3): 494-502.YANG Heng, WANG Qing, HE Zhoucan. Mutiple randomized sub-vectors quantization hashing for high-dimensional image feature matching[J]. Journal of Computer-Aided Deasign & Computer Graphics, 2010, 22(3): 494-502.
[13] LOWE D. OpenSIFT—An open-source SIFT library.[2013-11-15]. http://robwhess.github.io/opensift/.
[14] The Visual Geometry Group. Affine covariant features. (2007-07-15)[2013-11-15]. http://www.robots.ox.ac.uk/~vgg/research/affine/index.html.
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