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ImageSemantics : User-Generated Metadata, Content-Based Retrieval & Beyond

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Ralf Klamma
Ralf Klamma

ImageSemantics : User-Generated Metadata, Content-Based Retrieval & Beyond Marc Spaniol, Ralf Klamma, Mathias Lux TRIPLE-I, Graz, Austria, September 7, 2007

TechnologieFormation
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Ralf Klamma , Marc Spaniol Mathias Lux RWTH Aachen University University of Klagenfurt http://www.multimedia-metadata.info I-Media 2007 Graz, September 7, 2007 ImageSemantics : User-Generated Metadata, Content-Based Retrieval & Beyond
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Flickr.com ,[object Object],[object Object],Tags: gardenia, green Very dífferent results
Caliph & Emir ,[object Object],[object Object],Visualization of low-level features Not very similar
Web 2.0 Image Mining: ImageSemantics ,[object Object]
Problem Definition (Image Mining) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Multimedia semantics as rule-based system }
Requirements ,[object Object],[object Object],[object Object],[object Object]
State-of-the-Art ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Rule Component Data Management Maschine Learning OWL Representation XML Database Reasoner Testing
MPEG-7 Feature Descriptors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Rule Extraction Algorithm Clustering for low-level features Training data Sub rule extraction for low-level features Clustering for tags in the low-level cluster Sub rule extraction for tags Complete rule extraction
OWL Rule Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0 100
OWL Representation of Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],centroid Low-level Cluster max. Distance min. Distance
OWL Representation of Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Lowlevel_A_TagContent_Cluster
Representation of Rules in First Order Logic ,[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions & Outlook ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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ImageSemantics : User-Generated Metadata, Content-Based Retrieval & Beyond

  • 1. Ralf Klamma , Marc Spaniol Mathias Lux RWTH Aachen University University of Klagenfurt http://www.multimedia-metadata.info I-Media 2007 Graz, September 7, 2007 ImageSemantics : User-Generated Metadata, Content-Based Retrieval & Beyond
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  • 9. Rule Component Data Management Maschine Learning OWL Representation XML Database Reasoner Testing
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  • 11. Rule Extraction Algorithm Clustering for low-level features Training data Sub rule extraction for low-level features Clustering for tags in the low-level cluster Sub rule extraction for tags Complete rule extraction
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Notes de l'éditeur

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