Flexible rate allocation for local binary feature compression

Dominik Van Opdenbosch, Eckehard Steinbach

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Numerous real-time applications in computer vision rely on finding correspondences between local binary features. In many mobile scenarios, the visual information captured at a sensor node needs to be transmitted to a processing server, which is capable of storing the visual information or executing a complex analysis task. However, not necessarily all the visual information need to be transmitted. In this paper, we present a rate allocation scheme that is capable of categorizing features into classes according to their usefulness and select the amount of data spent on each class to maximize the overall performance of a computer vision task. We demonstrate the approach using ORB, BRISK, and FREAK features and show the improvements on a homography estimation task.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages3259-3263
Number of pages5
ISBN (Electronic)9781479970612
DOIs
StatePublished - 29 Aug 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
Country/TerritoryGreece
CityAthens
Period7/10/1810/10/18

Keywords

  • ATC
  • Bag-of-Words
  • Coding
  • Feature coding
  • Visual features

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