Share Email Print
cover

Proceedings Paper

Using heterogeneous annotation and visual information for the benchmarking of image retrieval system
Author(s): Henning Müller; Paul Clough; William Hersh; Thomas Deselaers; Thomas M. Lehmann; Bruno Janvier; Antoine Geissbuhler
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Many image retrieval systems, and the evaluation methodologies of these systems, make use of either visual or textual information only. Only few combine textual and visual features for retrieval and evaluation. If text is used, it is often relies upon having a standardised and complete annotation schema for the entire collection. This, in combination with high-level semantic queries, makes visual/textual combinations almost useless as the information need can often be solved using just textual features. In reality, many collections do have some form of annotation but this is often heterogeneous and incomplete. Web-based image repositories such as FlickR even allow collective, as well as multilingual annotation of multimedia objects. This article describes an image retrieval evaluation campaign called ImageCLEF. Unlike previous evaluations, we offer a range of realistic tasks and image collections in which combining text and visual features is likely to obtain the best results. In particular, we offer a medical retrieval task which models exactly the situation of heterogenous annotation by combining four collections with annotations of varying quality, structure, extent and language. Two collections have an annotation per case and not per image, which is normal in the medical domain, making it difficult to relate parts of the accompanying text to corresponding images. This is also typical of image retrieval from the web in which adjacent text does not always describe an image. The ImageCLEF benchmark shows the need for realistic and standardised datasets, search tasks and ground truths for visual information retrieval evaluation.

Paper Details

Date Published: 16 January 2006
PDF: 12 pages
Proc. SPIE 6061, Internet Imaging VII, 606105 (16 January 2006); doi: 10.1117/12.660259
Show Author Affiliations
Henning Müller, Univ. and Hospitals of Geneva (Switzerland)
Paul Clough, Sheffield Univ. (United Kingdom)
William Hersh, Oregon Health and Science Univ. (United States)
Thomas Deselaers, Aachen Univ. of Technology (Germany)
Thomas M. Lehmann, National Institutes of Health (United States)
Bruno Janvier, Univ. of Geneva (Switzerland)
Antoine Geissbuhler, Univ. and Hospitals of Geneva (Switzerland)


Published in SPIE Proceedings Vol. 6061:
Internet Imaging VII
Simone Santini; Raimondo Schettini; Theo Gevers, Editor(s)

© SPIE. Terms of Use
Back to Top