Hunting moving targets: an extension to {B}ayesian methods in multimedia databases

Bibtex entry :

@techreport { VG:MSM1999a,
    author = { Wolfgang M{\"u}ller and David McG. Squire and Henning M{\"u}ller and Thierry Pun },
    title = { Hunting moving targets: an extension to {B}ayesian methods in multimedia databases },
    institution = { Computer Vision Group, Computing Centre, University of Geneva },
    year = { 1999 },
    number = { 99.03 },
    address = { rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland },
    month = { July },
    url = { },
    abstract = { It has been widely recognised that the difference between the level of abstraction of the formulation of a query (by example) and that of the desired result (usually an image with certain semantics) calls for the use of learning methods that try to bridge this gap. Cox \emph{et al.}~have proposed a Bayesian method to learn the user's preferences during each query. Cox \emph{et al.}\'s system, \texttt{PicHunter}, is designed for optimal performance when the user is searching for a fixed target image. The performance of the system was evaluated using target testing, which ranks systems according to the number of interaction steps required to find the target, leading to simple, easily reproducible experiments. There are some aspects of image retrieval, however, which are not captured by this measure. In particular, the possibility of query drift (i.e.~a moving target) is completely ignored. The algorithm proposed by Cox \emph{et al.}~does not cope well with a change of target at a late query stage, because it is assumed that user feedback is noisy, but consistent. In the case of a moving target, however, the feedback is noisy \emph{and} inconsistent with earlier feedback. In this paper we propose an enhanced Bayesian scheme which selectively forgets inconsistent user feedback, thus enabling both the program and the user to ``change their minds''. The effectiveness of this scheme is demonstrated in moving target tests on a database of heterogeneous real-world images. },
    keywords = { relevance feedback, query drift, target testing, Bayesian methods, user modelling },
    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