A BOVW Based Query Generative Model

  • Reede Ren
  • John Collomosse
  • Joemon Jose
Conference paper

DOI: 10.1007/978-3-642-17832-0_12

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6523)
Cite this paper as:
Ren R., Collomosse J., Jose J. (2011) A BOVW Based Query Generative Model. In: Lee KT., Tsai WH., Liao HY.M., Chen T., Hsieh JW., Tseng CC. (eds) Advances in Multimedia Modeling. MMM 2011. Lecture Notes in Computer Science, vol 6523. Springer, Berlin, Heidelberg

Abstract

Bag-of-visual words (BOVW) is a local feature based framework for content-based image and video retrieval. Its performance relies on the discriminative power of visual vocabulary, i.e. the cluster set on local features. However, the optimisation of visual vocabulary is of a high complexity in a large collection. This paper aims to relax such a dependence by adapting the query generative model to BOVW based retrieval. Local features are directly projected onto latent content topics to create effective visual queries; visual word distributions are learnt around local features to estimate the contribution of a visual word to a query topic; the relevance is justified by considering concept distributions on visual words as well as on local features. Massive experiments are carried out the TRECVid 2009 collection. The notable improvement on retrieval performance shows that this probabilistic framework alleviates the problem of visual ambiguity and is able to afford visual vocabulary with relatively low discriminative power.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Reede Ren
    • 1
  • John Collomosse
    • 1
  • Joemon Jose
    • 2
  1. 1.CVSSPUniversity of SurreyGuildfordUK
  2. 2.IR GroupUniversity of GlasgowGlasgowUK

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