A simple bayesian framework for content-based image retrieval


Journal Article

We present a Bayesian framework for content-based image retrieval which models the distribution of color and texture features within sets of related images. Given a user-specified text query (e.g. "penguins") the system first extracts a set of images, from a labelled corpus, corresponding to that query. The distribution over features of these images is used to compute a Bayesian score for each image in a large unlabelled corpus. Unlabelled images are then ranked using this score and the top images are returned. Although the Bayesian score is based on computing marginal likelihoods, which integrate over model parameters, in the case of sparse binary data the score reduces to a single matrix-vector multiplication and is therefore extremely efficient to compute. We show that our method works surprisingly well despite its simplicity and the fact that no relevance feedback is used. We compare different choices of features, and evaluate our results using human subjects. © 2006 IEEE.

Full Text

Duke Authors

Cited Authors

  • Heller, KA; Ghahramani, Z

Published Date

  • December 22, 2006

Published In

Volume / Issue

  • 2 /

Start / End Page

  • 2110 - 2117

International Standard Serial Number (ISSN)

  • 1063-6919

Digital Object Identifier (DOI)

  • 10.1109/CVPR.2006.41

Citation Source

  • Scopus