Strategies for positive and negative relevance feedback in image retrieval

Bibtex entry :

@techreport { VG:MMS2000,
    author = { Henning M{\"u}ller and Wolfgang M{\"u}ller and David McG. Squire and St{\'e}phane Marchand-Maillet and Thierry Pun },
    title = { Strategies for positive and negative relevance feedback in image retrieval },
    institution = { Computer Vision Group, Computing Centre, University of Geneva },
    year = { 2000 },
    number = { 00.01 },
    address = { rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland },
    month = { jan },
    url = { },
    abstract = { Relevance feedback has been shown to be a very effective tool for enhancing retrieval results in text retrieval. In content-based image retrieval it is more and more frequently used and very good results have been obtained. However, too much negative feedback may destroy a query as good features get negative weightings. This paper compares a variety of strategies for positive and negative feedback. The performance evaluation of feedback algorithms is a hard problem. To solve this, we obtain judgments from several users and employ an automated feedback scheme. We can then evaluate different techniques using the same judgments. Using automated feedback, the ability of a system to adapt to the user's needs can be measured very effectively. Our study highlights the utility of negative feedback, especially over several feedback steps. },
    url1 = { },
    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