Content-based query of image databases: inspirations from text retrieval

Bibtex entry :

@article { VG:SMM2000a,
    author = { David McG. Squire and Wolfgang M{\"u}ller and Henning M{\"u}ller and Thierry Pun },
    title = { Content-based query of image databases: inspirations from text retrieval },
    journal = { Pattern Recognition Letters (Selected Papers from The 11th Scandinavian Conference on Image Analysis SCIA '99) },
    year = { 2000 },
    volume = { 21 },
    number = { 13-14 },
    pages = { 1193-1198 },
    note = { B.K. Ersboll, P. Johansen, Eds. },
    url = { },
    abstract = { This paper reports the application of techniques inspired by text retrieval research to content-based image retrieval. In particular, we show how the use of an inverted file data structure permits the use of an extremely high-dimensional feature-space, by restricting search to the subspace spanned by the features present in the query. A suitably sparse set of colour and texture features is proposed. A weighting scheme based on feature frequencies is used to combine disparate features in a compatible manner, and naturally extends to incorporate relevance feedback queries. The use of relevance feedback is shown consistently to improve system performance. },
    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