InforLorV4, Hal, Corpus, bibRecord, 005292

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

Identifieur interne : 005292 ( Hal/Corpus ); précédent : 005291; suivant : 005293

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

Auteurs : Sabine Barrat ; Salvatore Tabbone

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

Links to Exploration step

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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<idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno>
<idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno>
<idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno>
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<note type="proceedings" n="1">Yes</note>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Visual Features with Semantic Combination Using Bayesian Network for a More Effective Image Retrieval</title>
<author role="aut"><persName><forename type="first">Sabine</forename>
<surname>Barrat</surname>
</persName>
<email>sabine.barrat@loria.fr</email>
<idno type="halAuthorId">363424</idno>
<affiliation ref="#struct-2361"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Salvatore</forename>
<surname>Tabbone</surname>
</persName>
<email>tabbone@loria.fr</email>
<idno type="halAuthorId">150075</idno>
<orgName ref="#struct-300292"></orgName>
<affiliation ref="#struct-2361"></affiliation>
</author>
</analytic>
<monogr><meeting><title>ICPR</title>
<date type="start">2008-12-08</date>
<settlement>Tampa</settlement>
<country key="US">United States</country>
</meeting>
<imprint><date type="datePub">2008</date>
</imprint>
</monogr>
</biblStruct>
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<profileDesc><langUsage><language ident="en">English</language>
</langUsage>
<textClass><classCode scheme="halDomain" n="info.info-cv">Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</classCode>
<classCode scheme="halTypology" n="COMM">Conference papers</classCode>
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<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.</abstract>
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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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