Efficient access methods for content-based image retrieval with inverted files

Bibtex entry :

@techreport { VG:MSM1999,
    author = { Henning M{\"u}ller and David McG. Squire and Wolfgang M{\"u}ller and Thierry Pun },
    title = { Efficient access methods for content-based image retrieval with inverted files },
    institution = { Computer Vision Group, Computing Centre, University of Geneva },
    year = { 1999 },
    number = { 99.02 },
    address = { rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland },
    month = { July },
    url = { http://vision.unige.ch/publications/postscript/99/VGTR99.02_MuellerSquireMuellerPun.ps.gz },
    abstract = { As human factor studies over the last thirty years have shown, response time is a very important factor for the usability of an interactive system, especially on the world wide web. In particular, response times of under one second are often specified as a usability requirement \cite{Nie97}. This paper compares several methods for improving the evaluation time in a content-based image retrieval system (CBIRS) which uses inverted file technology. The use of the inverted file technology facilitates search pruning in a variety of ways, as is shown in this paper. For large databases ($> 2000$ images) and a high number of possible features ($> 80000$), efficient and fast access is necessary to allow interactive querying and browsing. Parallel access to the inverted file can reduce the response time. This parallel access is very easy to implement with little communication overhead, and thus scales well. Other search pruning methods, similar to methods used in information retrieval, can also reduce the response time significantly without reducing the performance of the system. The performance of the system is evaluated using precision vs. recall graphs, which are an established evaluation method in information retrieval. A user survey was carried out in order to obtain relevance judgments for the queries reported in this work. },
    keywords = { inverted file, content-based image retrieval, efficient access, search pruning, speed evaluation },
    url1 = { http://vision.unige.ch/publications/postscript/99/VGTR99.02_MuellerSquireMuellerPun.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