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research [2009/06/30 10:36]
marchand
research [2021/03/23 21:22]
marchand
Line 1: Line 1:
-{{keywords>​content-based affective emotion information collection image video multimedia retrieval visualisation CBIR CBR CBVR CBMR CBMIR  mining evaluation multimodal fusion research, gift recherche semantic SWKB  RDF OWL XML metadata auto-annotation description classification large scale}}+{{keywords>​information geometry ​content-based affective emotion information collection image video multimedia retrieval visualisation CBIR CBR CBVR CBMR CBMIR  mining evaluation multimodal fusion research, gift recherche semantic SWKB  RDF OWL XML metadata auto-annotation description classification large scale}}
  
-We address several aspects of multimedia information processing and management (see associate publications). 
-    * Video retrieval 
-    * Multimedia description and annotation 
-    * Collection guide 
-    * Image segmentation 
-    * Content-based image retrieval 
-We have developed the GIFT, an open-source package for Content-based image retrieval (CBIR) using the Query-by-Example (QBE) paradigm. 
  
-We have extensively worked on CBIR system evaluation, in close relationship with the Benchathlon NetworkIn this respect, the is a need for the existence of large annotated multimedia collectionsThis has led us to working on the problem ​of intelligent multimedia description,​ both from the knwoledge management and automated annotation viewpoints.+(see) {{https://​www.groundai.com/​project/​hypergraph-modeling-and-visualisation-of-complex-co-occurence-networks/​|Hypergraph modeling}} ​
  
-A major part of our research is also dedicated to video information management. We work on several aspects ​including+{{ :​demos:​corelhdme.png?800 |Manifold Learning}}  
-    * Temporal segmentation + 
-    * Event-based indexing +We address ​several aspects ​of information processing, learning and mining
-    * Content modelling and indexing +    * Data Mining 
-    * Large-scale storageaccess ​and retrieval +    * Feature space analysis 
-We have developed the novel concept of collection guiding to manage multimedia collections with truly accounting for the fact that the collection exists+    * Information geometry models for Machine learning 
 +    * Interactive information visualisation 
 +    * Information retrieval 
 + 
 +For more detailssee our [[research:​publications|publications]] ​and [[research:​projects|projects]]... 
 + 
 + 
 +{{ :​demos:​starshape.png?500 | Information Visualisation}}
research.txt · Last modified: 2021/03/23 21:22 by marchand
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Keywords: machine learning, information geometry, data mining, Big Data, affective information retrieval (recherche d'information), information visualisation, content-based image and video retrieval (CBIR, CBR, CBVR, CBMR, CBMIR), information mining, classification, multimedia and multimodal information management, semantic web, knowledge base (RDF, OWL, XML, metadata, auto-annotation, description), multimodal information fusion