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A comparative study of the performance of local feature-based pattern recognition algorithms

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Abstract

Several feature-based pattern recognition algorithms have been developed during the past decade. These algorithms rely on identifying keypoints in an image and assigning a descriptor to each point based on the composition of their surrounding region. Comparison of the descriptors of keypoints found in two images enables these algorithms to match similar objects within those images. The dependence of these algorithms’ performance on the similarity of the internal structure of objects makes them susceptible to modifications that change this internal structure. In this paper, we first compare the relative performance of some major feature-based algorithms in finding similar objects surrounded by geometrical noise. Next, we add several noise and transformation types to target objects and re-evaluate the performance of these algorithms under the resulting structural changes. Our results provide insights on the relative strengths of these algorithms in the presence and absence of several noise and transformation types. In addition, these findings allow us to identify modification types that can better inhibit the performance of these algorithms. The resulting insight can be used in applications that need to build resistance against such algorithms, e.g., in developing CAPTCHAs that need to be resistant to recognition attacks.

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Notes

  1. Scale-invariant feature transform.

  2. Speeded Up Robust Features.

  3. Features from accelerated segment test.

  4. Binary Robust Independent Elementary Features.

  5. Oriented FAST and Rotated BRIEF.

  6. Binary robust invariant scalable keypoints.

  7. Fast retina keypoint.

  8. Many applications do not need rotation invariance; they can use an upright version of SIFT which is faster.

  9. A region around a key point.

  10. Xs are one end and Ys are the other end of the lines.

  11. FAST keypoint orientation.

  12. Rotation-Aware Brief.

  13. The authors stated that BRISK’s method (selecting short-distance pairs as sampling pairs) results in choosing highly correlated pairs.

  14. Fisher’s F-ratio.

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Acknowledgments

This research is supported by NSERC (Natural Sciences and Engineering Research Council of Canada) and AITF (Alberta Innovates Technology Futures).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada

    Narges Roshanbin & James Miller

Authors
  1. Narges Roshanbin
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  2. James Miller
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Correspondence to James Miller.

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Roshanbin, N., Miller, J. A comparative study of the performance of local feature-based pattern recognition algorithms. Pattern Anal Applic 20, 1145–1156 (2017). https://doi.org/10.1007/s10044-016-0554-y

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