Improving Response Time by Search Pruning in a Content-Based Image Retrieval System, Using Inverted File Techniques

Bibtex entry :

@techreport { VG:SMM1999b,
    author = { David McG. Squire and Henning M{\"u}ller and Wolfgang M{\"u}ller },
    title = { Improving Response Time by Search Pruning in a Content-Based Image Retrieval System, Using Inverted File Techniques },
    institution = { Computer Vision Group, Computing Centre, University of Geneva },
    year = { 1999 },
    number = { 99.01 },
    address = { rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland },
    month = { February },
    url = { http://vision.unige.ch/publications/postscript/99/VGTR99.01_SquireMuellerMueller.ps.gz },
    abstract = { This paper describes several methods for improving query evaluation speed in a content-based image retrieval system (CBIRS). Response time is an extremely important factor in determining the usefulness of any interactive system, as has been demonstrated by human factors studies over the past thirty years. In particular, response times of less than one second are often specified as a usability requirement. It is shown that the use of inverted files facilitates the reduction of query evaluation time without significantly reducing the accuracy of the response. The performance of the system is evaluated using precision \vs recall graphs, which are an established evaluation method in information retrieval (IR), and are beginning to be used by CBIR researchers. },
    keywords = { content-based image retrieval, search pruning, inverted file, response time },
    url1 = { http://vision.unige.ch/publications/postscript/99/VGTR99.01_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