InforLorV4, Main, Merge, bibRecord, 003E54

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

Identifieur interne : 003E54 ( Main/Merge ); précédent : 003E53; suivant : 003E55

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

Auteurs : Sabine Barrat [France] ; Salvatore Tabbone [France]

Source :

RBID : Hal:inria-00339114

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.

Url:

https://hal.inria.fr/inria-00339114

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to stream Hal, to step Corpus: 005292
to stream Hal, to step Curation: 005292
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Hal:inria-00339114

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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Visual Features with Semantic Combination Using Bayesian Network for a More Effective Image Retrieval

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

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