Information Fusion in Multimedia Information Retrieval

Bibtex entry :

@inproceedings { Kludas:AMR07:IFMIR,
    author = { Jana Kludas and Eric Bruno and St{\'e}phane Marchand-Maillet },
    title = { Information Fusion in Multimedia Information Retrieval },
    booktitle = { Proceedings of 5th international Workshop on Adaptive Multimedia Retrieval (AMR) },
    year = { 2007 },
    address = { Paris, France },
    month = { July 5-6 },
    abstract = { In retrieval, indexing and classification of multimedia data an efficientinformation fusion of the different modalities is essential for thesystem's overall performance. Since information fusion, its influencefactors and performance improvement boundaries have been lively dis-cussed in the last years in different research communities, we willreview their latest findings. They most importantly point out thatexploiting the feature's and modality's dependencies will yield tomaximal performance. In data analysis and fusion tests with annotatedimage collections this is undermined. },
    url = { },

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