Advertisement

Video Retrieval Based on Words-of-Interest Selection

  • Lei Wang
  • Dawei Song
  • Eyad Elyan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6611)

Abstract

Query-by-example video retrieval is receiving an increasing attention in recent years. One of the state-of-art approaches is the Bag-of-visual Words (BoW) based technique, where images are described by a set of local features mapped to a discrete set of visual words. Such techniques, however, ignores spatial relations between visual words. In this paper, we present a content based video retrieval technique based on selected Words-of-Interest (WoI) that utilizes visual words spatial proximity constraint identified from the query. Experiments carried out on a public video database demonstrate promising results of our approach that outperform the classical BoW approach.

Keywords

Bag-of-Words Content based video retrieval Words-of-Interest 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sivic, J., Zisserman, A.: Efficient visual search for objects in videos. Proceedings of the IEEE (2008)Google Scholar
  2. 2.
    Zhang, S., Tian, Q., Hua, G., Huang, Q., Li, S.: Descriptive Visual Words and Visual Phrases for Image Applications. In: ACM MM (2009)Google Scholar
  3. 3.
    Liu, D., Chen, T.: Video retrieval based on object discovery. In: CVIU (2009)Google Scholar
  4. 4.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: CVIU, pp. 346–359 (2008)Google Scholar
  5. 5.
    Cao, L., Fei-Fei, L.: Spatially coherent latent topic model for concurrent object segmentation and classification. In: ICCV (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lei Wang
    • 1
  • Dawei Song
    • 1
  • Eyad Elyan
    • 1
  1. 1.School of ComputingThe Robert Gordon UniversityUnited Kingdom

Personalised recommendations