Semantic Context Inference in Multimedia Search
Identifieur interne : 000B23 ( Main/Exploration ); précédent : 000B22; suivant : 000B24Semantic Context Inference in Multimedia Search
Auteurs : Qianni Zhang [Royaume-Uni] ; Ebroul Izquierdo [Royaume-Uni]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: Multimedia content is usually complex and may contain many semantically meaningful elements interrelated to each other. Therefore to understand the high-level semantic meanings of the content, such interrelations need to be learned and exploited to further improve the search process. We introduce our ideas on how to enable automatic construction of semantic context by learning from the content. Depending on the targeted source of content, representation schemes for its semantic context can be constructed by learning from data. In the target representation scheme, metadata is divided into three levels: low, mid, and high levels. By using the proposed scheme, high-level features are derived out of the mid-level features. In order to explore the hidden interrelationships between mid-level and the high-level terms, a Bayesian network model is built using from a small amount of training data. Semantic inference and reasoning is then performed based on the model to decide the relevance of a video.
Url:
DOI: 10.1007/978-3-642-20898-0_28
Affiliations:
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Therefore to understand the high-level semantic meanings of the content, such interrelations need to be learned and exploited to further improve the search process. We introduce our ideas on how to enable automatic construction of semantic context by learning from the content. Depending on the targeted source of content, representation schemes for its semantic context can be constructed by learning from data. In the target representation scheme, metadata is divided into three levels: low, mid, and high levels. By using the proposed scheme, high-level features are derived out of the mid-level features. In order to explore the hidden interrelationships between mid-level and the high-level terms, a Bayesian network model is built using from a small amount of training data. Semantic inference and reasoning is then performed based on the model to decide the relevance of a video.</div></front></TEI><affiliations><list><country><li>Royaume-Uni</li></country><region><li>Angleterre</li><li>Grand Londres</li></region><settlement><li>Londres</li></settlement><orgName><li>Université de Londres</li></orgName></list><tree><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Zhang, Qianni" sort="Zhang, Qianni" uniqKey="Zhang Q" first="Qianni" last="Zhang">Qianni Zhang</name></region><name sortKey="Izquierdo, Ebroul" sort="Izquierdo, Ebroul" uniqKey="Izquierdo E" first="Ebroul" last="Izquierdo">Ebroul Izquierdo</name><name sortKey="Izquierdo, Ebroul" sort="Izquierdo, Ebroul" uniqKey="Izquierdo E" first="Ebroul" last="Izquierdo">Ebroul Izquierdo</name><name sortKey="Zhang, Qianni" sort="Zhang, Qianni" uniqKey="Zhang Q" first="Qianni" last="Zhang">Qianni Zhang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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