Performance Evaluation in Content-Based Image Retrieval: Overview and Proposals

Bibtex entry :

@article { VG:MMS2001,
    author = { Henning M{\"u}ller and Wolfgang M{\"u}ller and David McG. Squire and St{\'e}phane Marchand-Maillet and Thierry Pun },
    title = { Performance Evaluation in Content-Based Image Retrieval: Overview and Proposals },
    journal = { Pattern Recognition Letters (Special Issue on Image and Video Indexing) },
    year = { 2001 },
    volume = { 22 },
    number = { 5 },
    pages = { 593--601 },
    note = { H. Bunke and X. Jiang Eds. },
    url = { },
    abstract = { Evaluation of retrieval performance is a crucial problem in content-based image retrieval (CBIR). Many different methods for measuring the performance of a system have been created and used by researchers. This article discusses the advantages and shortcomings of the performance measures currently used. Problems such as defining a common image database for performance comparisons and a means of getting relevance judgments (or ground truth) for queries are explained. The relationship between CBIR and information retrieval (IR) is made clear, since IR researchers have decades of experience with the evaluation problem. Many of their solutions can be used for CBIR, despite the differences between the fields. Several methods used in text retrieval are explained. Proposals for performance measures and means of developing a standard test suite for CBIR, similar to that used in IR at the annual Text REtrieval Conference (TREC), are presented. (c) Copyright 2001, Elsevier Science, All rights reserved. },
    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