## 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 },
}`
--

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