Video retrieval using sparse Bayesian reconstruction

Pablo Ruiz*, S. Derin Babacan, Li Gao, Zhu Li, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

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

2 Scopus citations


Every day, a huge amount of video data is generated for different purposes and applications. Fast and accurate algorithms for efficient video search and retrieval are therefore essential. The interesting properties of sparse representation and the new sampling theory named Compressive Sensing (CS) constitute the core of the new approach to video representation and retrieval we are presenting in this paper. Once the representation (where sparsity is expected) has been chosen and the observations have been taken, the proposed approach utilizes Bayesian modeling and inference to tackle the retrieval problem. In order to speed up the inference process the use of Principal Components Analysis (PCA) to provide an alternative representation of the frames is analyzed. Experimental results validate the proposed approach whose robustness against noise is also examined.

Original languageEnglish (US)
Title of host publicationElectronic Proceedings of the 2011 IEEE International Conference on Multimedia and Expo, ICME 2011
StatePublished - 2011
Event2011 12th IEEE International Conference on Multimedia and Expo, ICME 2011 - Barcelona, Spain
Duration: Jul 11 2011Jul 15 2011

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X


Other2011 12th IEEE International Conference on Multimedia and Expo, ICME 2011


  • Bayesian inference
  • Bayesian modeling
  • Video retrieval
  • compressive sensing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


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