结合高阶图模型与蚁群优化的图像匹配方法

doi:10.3969/j.issn.1001-2400.2017.01.028

Abstract

Abstract:

Image matching is a fundamental problem in the computer vision field. This paper focuses on image matching based on the graph structure model. The methods of the graph model establishment in the second-order or high-order constraint are studied. In order to overcome the defects of traditional optimal algorithms which fall easily into the local optimal solution, this paper adopts the ant colony optimization algorithm to optimize the match score function and proposes an high-order graph matching algorithm based on ant colony optimization. It first applies the tensor matching algorithm to initialize the pheromone matrix to provide a good start point, adopts the affinity tensor to provide the priori knowledge for computing the heuristic factor, then calculates the transition probability using the pheromone and heuristic factor, and finally updates the pheromone in two ways by the solutions which have been searched. The two updating rules of pheromone are local and global. Experimental results show that this algorithm can get a higher matching accuracy and has a stronger robustness against deformation noises and outliers compared with others.

Key words: image matching, ant colony algorithm, high-order graph matching, optimization

YANG Siyan;CAO Wencan;LI Shiping. Second-order graph model ant and colony optimization based image matching[J].Journal of Xidian University, 2017, 44(1): 159-164.

References

［1］ ZHAN L L, YUE X. Research on Artificial Target Image Matching［C］//Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology: 2. Piscataway: IEEE Computer Society, 2009: 526-529.
［2］ XIN G, HONG X, YUAN L. An Improved Image Matching Target Tracking Algorithm［C］//2009 6th Web Information Systems and Applications Conference. Piscataway: IEEE Computer Society, 2009: 37-40.
［3］彭祺. 基于模式识别的双目立体视觉匹配研究［D］. 武汉: 武汉大学, 2013.
［4］傅颖. 导航制导中图像匹配算法的研究［D］. 成都: 电子科技大学, 2006.
［5］ EI BERBARI R. Functional and Anatomical Medical Image Matching［C］//2009 International Conference on Advances in Computational Tools for Engineering Applications, Piscataway: IEEE Computer Society, 2009: 648-651.
［6］ CHENG J, LENG C, WU J X, et al. Fast and Accurate Image Matching with Cascade Hashing for 3d Reconstruction［C］//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington: IEEE Computer Society, 2014:1-8.
［7］ FAKHERI M, SEDGHI T, SHAYESTEH M G, et al. Framework for Image Retrieval Using Machine Learning and Statistical Similarity Matching Techniques［J］. IET Image Processing, 2013, 7(1): 1-11.
［8］ WANG C Y, SU X H, WEN Y, et al. Research on Algorithm of Image Matching Based on Distance of Information-state Transition［C］//Proceedings of the 2011 International Conference on Computer Science and Service System, Piscataway: IEEE Computer Society, 2011: 604-606.
［9］ JANG J, EO T J, KIM M, et al. Medical Image Matching Using Variable Randomized Undersampling Probability Pattern in Data Acquisition［C］//Proceedings of the 13th International Conference on Electronics, Information and Communications. Piscataway: IEEE, 2014: 6914453.
［10］ FU G Y, FU W S, WU H Z, et al. A Fast Parallel Image Matching Algorithm Based on MPP Computing Model［C］//Proceedings of the 2012 IEEE International Conference on Computer Science and Automation Engineering. Washington: IEEE Computer Society, 2012, 765-769.
［11］ FIELDING G, KAM M. Weighted Matching for Dense Stereo Correspondence ［J］. Pattern Recognition, 2000, 33(9):1511-1524.
［12］ GONG M G, ZHAO S M, JIAO L C, et al. A Novel Coarse-to-fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7): 4328-4338.
［13］ LOWE D G. Image Features from Scale-invariant Keypoints［J］. International Journal on Computer Vision, 2004, 60(2): 91-110.
［14］ FISCHLER M A, BOLLES R C. Random Sample Consensus: a Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography［J］. Communications of the ACM, 1981, 24(6): 381-395.
［15］ GRIMSON W E L, LOZANO PEREZ T. Localizing Overlapping Parts by Searching the Interpretation Tree［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(4): 469-482.
［16］ LEORDEANU M, HEBERT M. A Spectral Technique for Correspondence Problems Using Pairwise Constraints ［C］//Proceedings of the International Conference on Computer Vision: 2. Piscataway: IEEE, 2005:1482-1489.
［17］ ZASS R, SHASHUA A. Probabilistic Graph and Hypergraph Matching［C］//26th IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2008: 4587500.
［18］ DUCHENNE O, BACH F, KWEON I S, et al. A Tensor-based Algorithm for High-order Graph Matching［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2383-2395.
［19］ PARK S, PARK S K, HEBERT M. Fast and Scalable Approximate Spectral Matching for Higher-order Graph Matching［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(3): 479-492.
［20］ PARPINELLI R S, LOPES H S, FREITAS A A. Data Mining with an Ant Colony Optimization Algorithm ［J］. IEEE Transactions on Evolutionary Computation, 2002, 6(4): 321-332.

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Second-order graph model ant and colony optimization based image matching

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