基于扩展级联原始位置敏感散列的快速影像特征匹配

doi:10.3969/j.issn.1000-3428.2016.08.038

摘要/Abstract

摘要： 为解决传统基于尺度不变特征变换(SIFT)的影像匹配算法实时性较差、效率不高的问题,提出一种扩展的级联位置敏感散列(LSH)影像特征匹配算法。通过提出的数据空间浮动二分哈希构建一种比原始LSH具有更高位置敏感性的投影空间,实现对高维特征数据的划分,仅在高相似度集合中进行查询,从而提高检索速度。在各类特征集合内部进行二次随机投影散列,将特征映射到具有更好局部敏感性的高维海明空间,采用汉明距离和欧式距离相结合的测度方法,完成匹配特征对的快速查找和精确计算。实验结果表明,扩展的级联LSH影像特征匹配算法在匹配精度高于最佳Bin优先和LSH的基础上,匹配速度提高约2.5倍~3倍。

关键词: 尺度不变特征变换, 图像匹配, 扩展的级联位置敏感散列, 二分哈希, 汉明距离

Abstract: In order to solve the problem of poor real-time performance and low efficiency of the traditional image matching algorithm based on Scale Invariant Feature Transform(SIFT),an extended cascade Locality Sensitive Hashing(LSH) image feature matching algorithm is proposed.A data space floating dichotomy hash is used to build a projection space with higher locality sensitivity than the original LSH,which achieves the partition for high dimensional feature data and makes the query process only conduct in a high similarity set so as to increase the retrieval speed.Quadratic random projection hashing is adopted in all kinds of feature sets to map the features into a higher dimensional Hamming space,which has better locality sensitivity.The measurement method is used by combination of Hamming distance and Euclidean distance to complete the fast search and precise calculation.Experimental results indicate the extended cascade LSH image feature matching algorithm speeds up 2.5 times to 3 times while getting higher matching accuracy than the traditional Best Bin First(BBF) and LSH based method.

Key words: Scale Invariant Feature Transform(SIFT), image matching, extended cascade Locality Sensitive Hashing(LSH), dichotomy hash, Hamming distance

中图分类号:

TP391.41

杨凯,陈丽芳,刘渊. 基于扩展级联原始位置敏感散列的快速影像特征匹配[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2016.08.038.

YANG Kai,CHEN Lifang,LIU Yuan. Fast Image Feature Matching Based on Extended Cascade Original Locality Sensitive Hashing[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2016.08.038.

https://www.ecice06.com/CN/Y2016/V42/I8/211

参考文献

参考文献［1］Lowe D G.Distinctive Image Features from Scale-invariant Keypoints［J］.International Journal of Computer Vision,2004,60(2):91-110. ［2］Brown M,Lowe D G.Automatic Panoramic Image Stitching Using Invariant Features［J］.International Journal of Computer Vision,2007,74(1):59-73. ［3］Lucchese L,Leorin S,Cortelazzo G M,et al.Estimation of Two-dimensional Affine Transformations Through Polar Curve Matching and Its Application to Image Mosaicking and Remote-sensing Data Registration［J］.IEEE Transactions on Image Processing,2006,15(10):3008-3019. ［4］邓宝松,高宇,杨冰,等.序列图像中三维空间点的精确重建方法［J］.计算机工程,2007,33(11):18-20. ［5］王士同,邓赵红.模糊规则自适应学习的弹性图像配准［J］.江南大学学报:自然科学版,2007,6(6):631-636. ［6］Schmid C,Mikolajczyk K.A Performance Evaluation of Local Descriptors［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630. ［7］Grauman K,Darrell T.Efficient Image Matching with Distributions of Local Invariant Features［C］//Pro-ceedings of IEEE Computer Society Conference on Computer Vision & Pattern Recognition.Washington D.C.,USA:IEEE Press,2005:627-634. ［8］Lindeberg T.Scale-space for Discrete Signals［J］.IEEE Transactions on Pattern Analysis and Machine Intel-ligence,1990,12(3):234-254. ［9］Muja M,Lowe D G.Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration［C］//Proceedings of Visapp International Conference on Com-puter Vision Theory and Applications.Washington D.C.,USA:IEEE Press,2009:331-340. ［10］Beis J S,Lowe D G.Shape Indexing Using Approximate Nearest-neighbour Search in High-dimensional Spaces［C］//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,1997:1000-1006. ［11］Ke Y,Sukthankar R.PCA-SIFT:A More Distinctive Representation for Local Image Descriptors［C］//Pro-ceedings of IEEE Computer Society Conference on Com-puter Vision and Pattern Recognition.Washington,D.C.,USA:IEEE Computer Society,2004:506-513. ［12］杨薇,董洪伟,刘蕾,等.基于 SIFT和Daisy相结合的立体匹配算法［J］.计算机工程与应用,2014,50(12):147-150. ［13］Indyk P,Motwani R.Approximate Nearest Neighbors:Towards Removing the Curse of Dimensionality［C］//Proceedings of the 30th Annual ACM Symposium on Theory of Computing.New York,USA:ACM Press,1998:604-613. ［14］Gionis A,Indyk P,Motwani R,et al.Similarity Search in High Dimensions via Hashing［C］//Proceedings of the 25th International Conference on Very Large Data Bases.San Francisco,USA:Morgan Kaufmann Publishers Inc.,1999:518-529. ［15］Auclair A,Cohen L D,Vincent N.How to Use SIFT Vectors to Analyze an Image with Database Tem-plates［C］//Proceedings of the 5th International Workshop on Adaptive Multimedia Retrieval.Berlin,Germany:Springer,2008:224-236. ［16］Slaney M,Casey M.Locality-sensitive Hashing for Finding Nearest Neighbors［J］.IEEE Signal Processing Magazine,2010,25(2):128-131. ［17］龚卫国,张旋,李正浩,等.基于改进局部敏感散列算法的图像配准［J］.光学·精密工程,2011,19(6):1375-1383. ［18］曹玉东,刘福英,蔡希彪,等.基于局部敏感哈希算法的图像高维数据索引技术的研究［J］.辽宁工业大学学报:自然科学版,2013(1):1-3. ［19］何周灿,王庆,杨恒,等.一种面向快速图像匹配的扩展LSH算法［J］.四川大学学报:自然科学版,2010,47(2):269-274. ［20］Muja M,Lowe D G.Scalable Nearest Neighbor Algorithms for High Dimensional Data［J］.IEEE Transactions on Pattern Analysis & Machine Intel-ligence,2014,36(11):2227-2240. ［21］陶剑文,王士同.L1范局部线性嵌入［J］.中国图象图形学报,2011,16(10):1802-1811. ［22］安亚静,王士同.引入指数p的局部保持映射算法［J］.计算机工程,2011,37(17):178-180. 编辑顾逸斐

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