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Performance Evaluation of Local Descriptors for Affine Invariant Region Detector

  • Man Hee Lee
  • In Kyu ParkEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9008)

Abstract

Local feature descriptors are widely used in many computer vision applications. Over the past couple of decades, several local feature descriptors have been proposed which are robust to challenging conditions. Since they show different characteristics in different environment, it is necessary to evaluate their performance in an intensive and consistent manner. However, there has been no relevant work that addresses this problem, especially for the affine invariant region detectors which are popularly used in object recognition and classification. In this paper, we present a useful and rigorous performance evaluation of local descriptors for affine invariant region detector, in which MSER (maximally stable extremal regions) detector is employed. We intensively evaluate local patch based descriptors as well as binary descriptors, including SIFT (scale invariant feature transform), SURF (speeded up robust features), BRIEF (binary robust independent elementary features), FREAK (fast retina keypoint), Shape descriptor, and LIOP (local intensity order pattern). Intensive evaluation on standard dataset shows that LIOP outperforms the other descriptors in terms of precision and recall metric.

Keywords

Interest Point Local Descriptor Region Detector Sift Descriptor Deformable Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported by the IT R&D program of MSIP/ KEIT. [10047078, 3D reconstruction technology development for scene of car accident using multi view black box image].

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Information and Communication EngineeringInha UniversityIncheonKorea

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