Object-based image retrieval using the statistical structure of images

Derek Hoiem, Rahul Sukthankar, Henry Schneiderman, Larry Huston

Research output: Contribution to journalConference articlepeer-review

Abstract

We propose a new Bayesian approach to object-based image retrieval with relevance feedback. Although estimating the object posterior probability density from few examples seems infeasible, we are able to approximate this density by exploiting statistics of the image database domain. Unlike previous approaches that assume an arbitrary distribution for the unconditional density of the feature vector (the density of the features taken over the entire image domain), we learn both the structure and the parameters of this density. These density estimates enable us to construct a Bayesian classifier. Using this Bayesian classifier, we perform a windowed scan over images for objects of interest and employ the user's feedback on the search results to train a second classifier that focuses on eliminating difficult false positives. We have incorporated this algorithm into an object-based image retrieval system. We demonstrate the effectiveness of our approach with experiments using a set of categories from the Corel database.

Original languageEnglish (US)
Pages (from-to)II490-II497
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2
StatePublished - Oct 19 2004
Externally publishedYes
EventProceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004 - Washington, DC, United States
Duration: Jun 27 2004Jul 2 2004

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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