Content-based query of image databases, inspirations from text retrieval: inverted files, frequency-based weights and relevance feedback

Bibtex entry :

@techreport { VG:SMM1998,
    author = { David McG. Squire and Wolfgang M{\"u}ller and Henning M{\"u}ller and Jilali Raki },
    title = { Content-based query of image databases, inspirations from text retrieval: inverted files, frequency-based weights and relevance feedback },
    institution = { Computer Vision Group, Computing Centre, University of Geneva },
    year = { 1998 },
    number = { 98.04 },
    address = { rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland },
    month = { November },
    url = { http://vision.unige.ch/publications/postscript/98/VGTR98.04_SquireMuellerMuellerRaki.ps.gz },
    abstract = { In this paper we report the application of techniques inspired by text retrieval research to the content-based query of image databases. In particular, we show how the use of an inverted file data structure permits the use of a feature space of $\mathcal{O}(104)$ dimensions, 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 scheme based on the frequency of occurrence of features in both individual images and in the whole collection provides a means of weighting possibly incommensurate 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, as measured by precision and recall. },
    url1 = { http://vision.unige.ch/publications/postscript/98/VGTR98.04_SquireMuellerMuellerRaki.pdf },
    vgclass = { report },
    vgproject = { viper },
}