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Reducing Keypoint Database Size

  • Shahar Jamshy
  • Eyal Krupka
  • Yehezkel Yeshurun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

Keypoints are high dimensional descriptors for local features of an image or an object. Keypoint extraction is the first task in various computer vision algorithms, where the keypoints are then stored in a database used as the basis for comparing images or image features. Keypoints may be based on image features extracted by feature detection operators or on a dense grid of features. Both ways produce a large number of features per image, causing both time and space performance challenges when upscaling the problem.

We propose a novel framework for reducing the size of the keypoint database by learning which keypoints are beneficial for a specific application and using this knowledge to filter out a large portion of the keypoints. We demonstrate this approach on an object recognition application that uses a keypoint database. By using leave one out K nearest neighbor regression we significantly reduce the number of keypoints with relatively small reduction in performance.

Keywords

Keypoints Saliency Recognition ALOI 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shahar Jamshy
    • 1
  • Eyal Krupka
    • 2
  • Yehezkel Yeshurun
    • 1
  1. 1.School of Computer ScienceTel Aviv UniversityTel AvivIsrael
  2. 2.Israel Innovation LabsMicrosoft Israel R&D CenterIsrael

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