## Relevance feedback and term weighting schemes for content-based image retrieval

### Bibtex entry :

@techreport { VG:SMM1998a,
author = { David McG. Squire and Wolfgang M{\"u}ller and Henning M{\"u}ller },
title = { Relevance feedback and term weighting schemes for content-based image retrieval },
institution = { Computer Vision Group, Computing Centre, University of Geneva },
year = { 1998 },
number = { 98.05 },
address = { rue G\'en\'eral Dufour, 24, CH-1211 Gen\eve, Switzerland },
month = { December },
url = { http://vision.unige.ch/publications/postscript/98/VGTR98.05_SquireMuellerMueller.ps.gz },
abstract = { This paper describes the application of techniques derived from text retrieval research to the content-based querying of image databases. Specifically, the use of inverted files, frequency-based weights and relevance feedback are investigated. The use of inverted files allows very large numbers ($\geq \mathcal{O}(104)$) of \emph{possible} features to be used. since search is limited to the subspace spanned by the features present in the query image(s). A variety of weighting schemes 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 results significantly, as measured by precision and recall, for all users. },
url1 = { http://vision.unige.ch/publications/postscript/98/VGTR98.05_SquireMuellerMueller.pdf },
vgclass = { report },
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