Implementation of a fast image coding and retrieval system using a GPU

Prasanna Sattigeri, Jayaraman J. Thiagarajan, Karthikeyan N. Ramamurthy, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Sparse coding of image patches is a compact but computationally expensive method of representing images. As part of our SenSIP consortium industry projects, we implement the Orthogonal Matching Pursuit algorithm using a single CUDA kernel on a GPU and sparse codes for image patches are obtained in parallel. Image-based exact search and visually similar search using the image patch sparse codes are performed. Results demonstrate large speed-up over CPU implementations and good retrieval performance is also achieved.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Emerging Signal Processing Applications, ESPA 2012 - Proceedings
Pages5-8
Number of pages4
DOIs
StatePublished - Mar 15 2012
Event2012 IEEE International Conference on Emerging Signal Processing Applications, ESPA 2012 - Las Vegas, NV, United States
Duration: Jan 12 2011Jan 14 2011

Publication series

Name2012 IEEE International Conference on Emerging Signal Processing Applications, ESPA 2012 - Proceedings

Other

Other2012 IEEE International Conference on Emerging Signal Processing Applications, ESPA 2012
CountryUnited States
CityLas Vegas, NV
Period1/12/111/14/11

Keywords

  • GPU implementation
  • Sparse coding
  • image retrieval
  • orthogonal matching pursuit

ASJC Scopus subject areas

  • Signal Processing

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