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Locality Preserving Matching

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Abstract

Seeking reliable correspondences between two feature sets is a fundamental and important task in computer vision. This paper attempts to remove mismatches from given putative image feature correspondences. To achieve the goal, an efficient approach, termed as locality preserving matching (LPM), is designed, the principle of which is to maintain the local neighborhood structures of those potential true matches. We formulate the problem into a mathematical model, and derive a closed-form solution with linearithmic time and linear space complexities. Our method can accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds. To demonstrate the generality of our strategy for handling image matching problems, extensive experiments on various real image pairs for general feature matching, as well as for point set registration, visual homing and near-duplicate image retrieval are conducted. Compared with other state-of-the-art alternatives, our LPM achieves better or favorably competitive performance in accuracy while intensively cutting time cost by more than two orders of magnitude.

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Notes

  1. https://sites.google.com/site/jiayima2013/home.

  2. The distribution of initial inlier percentages on the test data can be seen from the precision curve at \(\lambda =1\) in Fig. 2 as in this case all putative matches are considered as inliers.

  3. For real-world tasks such as multiple view stereo and SLAM, a better metric would be to use the inliers to retrieve the camera pose from stereo images and evaluate their accuracy (Bian et al. 2017). However, such camera pose estimation usually relies on an additional robust estimator such as RANSAC, which may not directly characterize the matching performance. Therefore, for the purpose of general feature matching, we only use precision and recall to characterize the performance.

  4. http://www.ti.uni-bielefeld.de/html/research/avardy/index.html.

  5. As different feature extraction used in this paper, the performance of HiSS (Churchill and Vardy 2013) and SSVS (Liu et al. 2013) is not exactly the same as reported in the original papers. In addition, the reimplemented SSVS method in this paper does not contain the mismatch removal introduced in (Liu et al. 2013).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61773295, 61503288, 61501413, 41501505 and 61772512, and the Beijing Advanced Innovation Center for Intelligent Robots and Systems under Grant No. 2016IRS15.

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Authors and Affiliations

  1. Electronic Information School, Wuhan University, Wuhan, 430072, China

    Jiayi Ma

  2. ReadSense Ltd., Shanghai, 200040, China

    Ji Zhao

  3. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Junjun Jiang

  4. Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, 430073, China

    Huabing Zhou

  5. School of Computer Software, Tianjin University, Tianjin, 300350, China

    Xiaojie Guo

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  1. Jiayi Ma
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  2. Ji Zhao
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  3. Junjun Jiang
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  4. Huabing Zhou
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  5. Xiaojie Guo
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Correspondence to Xiaojie Guo.

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Ma, J., Zhao, J., Jiang, J. et al. Locality Preserving Matching. Int J Comput Vis 127, 512–531 (2019). https://doi.org/10.1007/s11263-018-1117-z

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