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Visual Features with Semantic Combination Using Bayesian Network for a More Effective Image Retrieval

Sabine Barrat 1 Salvatore Tabbone 1
1 QGAR - Querying Graphics through Analysis and Recognition
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In many vision problems, instead of having fully annotated training data, it is easier to obtain just a subset of data with annotations, because it is less restrictive for the user. For this reason, in this paper, we consider especially the problem of weakly-annotated image retrieval, where just a small subset of the database is annotated with keywords. We present and evaluate a new method which improves the effectiveness of content-based image retrieval, by integrating semantic concepts extracted from text. Our model is inspired from the probabilistic graphical model theory: we propose a hierarchical mixture model which enables to handle missing values and to capture the user's preference by also considering a relevance feedback process. Results of visual-textual retrieval associated to a relevance feedback process, reported on a database of images collected from the Web, partially and manually annotated, show an improvement of about 44.5% in terms of recognition rate against content-based retrieval.
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Contributor : Sabine Barrat <>
Submitted on : Sunday, November 16, 2008 - 9:24:20 PM
Last modification on : Friday, February 26, 2021 - 3:28:06 PM
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  • HAL Id : inria-00339114, version 1



Sabine Barrat, Salvatore Tabbone. Visual Features with Semantic Combination Using Bayesian Network for a More Effective Image Retrieval. ICPR, Dec 2008, Tampa, United States. ⟨inria-00339114⟩



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