Design and Evaluation of a Content-based Image Retrieval System

Bibtex entry :

@incollection { VG:SMM2001,
    author = { David McG. Squire and Henning M{\"u}ller and Wolfgang M{\"u}ller and St{\'e}phane Marchand-Maillet and Thierry Pun },
    title = { Design and Evaluation of a Content-based Image Retrieval System },
    chapter = { 7 },
    publisher = { Idea Group Publishing },
    year = { 2001 },
    editor = { Syed M Rahman },
    pages = { 125--151 },
    abstract = { The growth in size and accessibility of multimedia databases have changed our approach to information retrieval. Classical text-based systems show their limitations in the context of multimedia retrieval. In this chapter, we address the problem of conceiving and evaluating a content-based image retrieval system. First, we investigate the use of the query-by-example (QBE) paradigm as a base paradigm for the development of a content-based image retrieval system (CBIRS). We show that it should be considered as a complement to the classical textual-based paradigms. We then evaluate the capabilities of the most up-to-date computer vision techniques in contributing to the realisation of such a system. Further, beyond the necessity of accurate image understanding techniques, we show that the amount of data involved by the process of describing image content should also be considered as an important issue. This aspect of our study is largely based on the experience acquired by the text retrieval (TR) community, which we adapt to the context of CBIR. Similarly, the text retrieval community has also developed a significant experience in evaluating retrieval systems, where judgements include subjectivity and context dependency. Extending this experience, we study a coherent framework for performing the evaluation of a CBIRS. As a practical example, we use our Viper CBIR system, using a novel communication protocol called MRML to pinpoint the importance of the sharing of resource in facilitating the evaluation and therefore the development of CBIRS. },
    booktitle = { Design and Management of Multimedia Information Systems: Opportunities and Challenges },
    vgclass = { refpap },
    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