Relevance feedback and term weighting schemes for content-based imageretrieval

Bibtex entry :

@inproceedings { VG:SMM1999a,
    author = { David McG. Squire and Wolfgang M{\"u}ller and Henning M{\"u}ller },
    title = { Relevance feedback and term weighting schemes for content-based imageretrieval },
    booktitle = { Third International Conference On Visual Information Systems },
    pages = { 549--556 },
    year = { 1999 },
    address = { Amsterdam, The Netherlands },
    month = { 2--4~June },
    url = { },
    abstract = { This paper describes the application of techniques derived from textretrieval research to the content-based querying of image databases.Specifically, the use of inverted files, frequency-based weightsand relevance feedback are investigated. The use of inverted filesallows very large numbers ($\geq \mathcal{O}(104)$) of \emph{possible}features to be used. since search is limited to the subspace spannedby the features present in the query image(s). A variety of weightingschemes used in text retrieval are employed, yielding different results.We suggest possibles modifications for their use with image databases.The use of relevance feedback was shown to improve the query resultssignificantly, as measured by precision and recall, for all users. },
    owner = { steph },
    timestamp = { 2008.05.04 },
    url1 = { },
    vgclass = { refpap },
    vgproject = { viper },

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