Automatic Images Annotation Extension Using a Probabilistic Graphical Model
Identifieur interne : 000350 ( Main/Exploration ); précédent : 000349; suivant : 000351Automatic Images Annotation Extension Using a Probabilistic Graphical Model
Auteurs : Abdessalem Bouzaieni [France] ; Salvatore Tabbone [France] ; Sabine Barrat [France]Source :
English descriptors
- mix :
Abstract
With the fast development of digital cameras and social media image sharing, automatic image annotation has become a researcharea of great interest. It enables indexing, extracting and searching in large collections of images in an easier and faster way. In this paper, wepropose a model for the annotation extension of images using a probabilistic graphical model. This model is based on a mixture of multinomialdistributions and mixtures of Gaussians. The results of the proposed model are promising on three standard datasets: Corel-5k, ESP-Gameand IAPRTC-12.
Url:
DOI: 10.1007/978-3-319-23117-4_50
Affiliations:
- France
- Centre-Val de Loire, Grand Est, Lorraine (région), Région Centre
- Metz, Nancy, Tours
- Centre Val de Loire Université, Université de Lorraine, Université de Tours
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Le document en format XML
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Tours</orgName><desc><address><addrLine>60 rue du Plat d'Étain, 37020 Tours cedex 1 </addrLine><country key="FR"></country></address><ref type="url">http://www.univ-tours.fr</ref></desc></org></tutelle><tutelle active="#struct-300408" type="direct"><org type="institution" xml:id="struct-300408" status="VALID"><orgName>Polytech'Tours</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Tours</settlement><region type="old region" nuts="2">Région Centre</region><region type="region" nuts="2">Centre-Val de Loire</region></placeName><orgName type="university">Université de Tours</orgName><orgName type="institution" wicri:auto="newGroup">Centre Val de Loire Université</orgName></affiliation></author></analytic><idno type="DOI">10.1007/978-3-319-23117-4_50</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term> Gaussian mixtures</term><term> annotation extension</term><term> probabilistic graphical models</term><term> textual characteristics.</term><term> visual characteristics</term><term>image annotation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">With the fast development of digital cameras and social media image sharing, automatic image annotation has become a researcharea of great interest. It enables indexing, extracting and searching in large collections of images in an easier and faster way. In this paper, wepropose a model for the annotation extension of images using a probabilistic graphical model. This model is based on a mixture of multinomialdistributions and mixtures of Gaussians. The results of the proposed model are promising on three standard datasets: Corel-5k, ESP-Gameand IAPRTC-12.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Centre-Val de Loire</li><li>Grand Est</li><li>Lorraine (région)</li><li>Région Centre</li></region><settlement><li>Metz</li><li>Nancy</li><li>Tours</li></settlement><orgName><li>Centre Val de Loire Université</li><li>Université de Lorraine</li><li>Université de Tours</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Bouzaieni, Abdessalem" sort="Bouzaieni, Abdessalem" uniqKey="Bouzaieni A" first="Abdessalem" last="Bouzaieni">Abdessalem Bouzaieni</name></region><name sortKey="Barrat, Sabine" sort="Barrat, Sabine" uniqKey="Barrat S" first="Sabine" last="Barrat">Sabine Barrat</name><name sortKey="Tabbone, Salvatore" sort="Tabbone, Salvatore" uniqKey="Tabbone S" first="Salvatore" last="Tabbone">Salvatore Tabbone</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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