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An adaptive image feature matching method using mixed Vocabulary-KD tree

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Abstract

This paper proposes an adaptive scale-invariant feature matching method based on data clustering, to solve the problem of low robustness of the KD tree matching method caused by SIFT feature noise sensitivity, and our method can be used to AR applications. The method has two stages: offline data re-clustering and online two-stage feature matching. This paper is the first to present a Vocabulary-KD data structure which achieves SIFT using KD tree by tuning the number of features of the Vocabulary nodes. Moreover, based on the Vocabulary-KD data structure, an adaptive feature matching method is proposed, which is consist of two clustering, one on the feature sets and the other on the feature sets contained by the leaf nodes of the Vocabulary-KD tree, along with adaptive adjustment of the relevant parameters of the Vocabulary-KD tree. At last, key images are selected in real-time for the second stage feature matching. The different results show that the proposed method can effectively resist noise, improve the adaptivity of the SIFT feature matching method, so as to achieve the trade-off between efficiency and robustness.

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References

  1. Baumberg A. (2000) Reliable feature matching across widely separated views//Proceedings of the Conference on Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina, USA, 2000. Hilton Head Island: IEEE Computer Society: 774–781

  2. Cheng Z, Chang X, Zhu L, Kankanhalli M (2018) MMALFM. Explainable recommendation by leveraging reviews and images. ACM Transactions on Information Systems (TOIS) 37(2):16

    Google Scholar 

  3. Choudhary S, Ojha N. (2017) Real-time crowd behavior detection using SIFT feature extraction technique in video sequences, 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), 936–940

  4. Deng W, Yao R, Zhao H, Yang X, Li G. (2017) A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Computing

  5. Deng W, Zhang S, Zhao H, Yang X (2018) A novel fault diagnosis method based on integrating empirical wavelet transform and fuzzy entropy for motor bearing. IEEE Access 6(1):35042–35056

    Article  Google Scholar 

  6. Deng W, Xu J, Zhao H. (2019) An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE ACCESS

  7. Dinh V, Nguyen T, Jeon J (2019) Rectification Using Different Types of Cameras Attached to a Vehicle. IEEE Trans Image Process 28(2):815–826

    Article  MathSciNet  Google Scholar 

  8. Guo Y, Cheng Z, Nie L, Wang Y, Ma J, Kankanhalli M (2018) Attentive Long Short-Term Preference Modeling for Personalized Product Search. ACM Transactions on Information Systems (TOIS) 37(2):19

    Google Scholar 

  9. Harris C, Stephens M. (1988) A combined corner and edge detector//Proceedings of the 4th Alvey Vision Conference, Manchester, 1998. MA, USA: Butterworth-Heinemann Newton:147–151

  10. Jain A, Murty D (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323

    Article  Google Scholar 

  11. Jiang J, Li X, Zhang G (2014) SIFT Hardware Implementation for Real-Time Image Feature Extraction. IEEE Trans. Circuits Syst. Video Technol. 24(7):1209–1220

    Article  MathSciNet  Google Scholar 

  12. Kelsey R, Clinger W, Rees J (1998) Revised Report on the Algorithmic Language Scheme. Higher-Order and Symbolic Computation 11(1):7–105

    Article  Google Scholar 

  13. Lee DT, Wong CK (1977) Worst-case analysis for region and partial region searches in multidimensional binary search trees and balanced quad trees. Acta Informatica 9(1):23–29

    Article  MathSciNet  Google Scholar 

  14. Lindeberg T (1998) Feature detection with automatic scale selection. Int J Comput Vis 30(2):79–116

    Article  Google Scholar 

  15. Liu P, Yu H, Cang S (2018) Optimized adaptive tracking control for an underactuated vibro-driven capsule system. Nonlinear Dynamics 94(3):1803–1817

    Article  Google Scholar 

  16. Liu P, Yu H, Cang S (2018) Trajectory synthesis and optimization of an underactuated microrobotic system with dynamic constraints and couplings. Int J Control Autom Syst 16(5):2373–2383

    Article  Google Scholar 

  17. Liu P, Yu H, Cang S (2019) Modelling and analysis of dynamic frictional interactions of vibro-driven capsule systems with viscoelastic property. Eur J Mech A Solids 74:16–25

    Article  MathSciNet  Google Scholar 

  18. Luo S, Mou W, Althoefer K (2015) Novel Tactile-SIFT Descriptor for Object Shape Recognition. IEEE Sensors J 15(9):5001–5009

    Article  Google Scholar 

  19. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630

    Article  Google Scholar 

  20. Mustafa A, Kim H (2019) MSFD: Multi-Scale Segmentation-Based Feature Detection for Wide-Baseline Scene Reconstruction. IEEE Trans Image Process 28(3):1118–1132

    Article  MathSciNet  Google Scholar 

  21. Nister D, Stewenius H (2006) Scalable recognition with a vocabulary tree, Proceedings of the CVPR’2006, IEEE press, 2161–2168

  22. Schaffalitzky F, Zisserman A. (2002) Multi-view matching for unordered image sets//Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, 2002. Copenhagen:Springer:414–431

  23. Schindler G, Brown M, Szeliski R (2007) City-scale location recognition//In Proceedings of the Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA, 2007. Minneapolis: IEEE Computer Society: 1–7

  24. Tapas K, Mount DM, Netanyahu NS, Piatko CD, Silverman R, Wu AY (2002) An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7):881–892

    Article  Google Scholar 

  25. Toews M, Wells WIII, Collins DL, Arbel T (2010) Feature-based morphometry: Discovering group-related anatomical patterns. NeuroImage 49(3):2318–2327

    Article  Google Scholar 

  26. Tuytelaars T, Van Gool L (2004) Matching widely separated views based on affine invariant regions. Int J Comput Vis 59(1):61–85

    Article  Google Scholar 

  27. Wang R, Jeon J (2017) Design of real-time SIFT feature extraction, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, 3545–3549

Download references

Acknowledgements

This paper is supported by Beijing Social Science Foundation (No.18YTC038); Beijing Natural Science Foundation (No.4182018,No.4194076), the open funding project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No.VRLAB2018A05); Beijing Youth Talent Foundation (No.2016000026833ZK09), NCUT Foundation (No.XN018001).

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Authors and Affiliations

  1. School of computer science, North China University of Technology, Beijing, 100144, China

    Fengquan Zhang & Yahui Gao

  2. Airborne Remote Sensing Center, Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, Beijing, 100094, China

    Liuqing Xu

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  1. Fengquan Zhang
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  2. Yahui Gao
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  3. Liuqing Xu
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Correspondence to Fengquan Zhang.

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Zhang, F., Gao, Y. & Xu, L. An adaptive image feature matching method using mixed Vocabulary-KD tree. Multimed Tools Appl 79, 16421–16439 (2020). https://doi.org/10.1007/s11042-019-7438-2

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