Gilbert, A., Piras, L., Wang, J.K. orcid.org/0000-0003-0048-3893 et al. (6 more authors) (2016) Overview of the ImageCLEF 2016 Scalable Concept Image Annotation Task. In: Balog, K., Cappellato, L., Ferro, N. and Macdonald, C., (eds.) CLEF 2016 Working Notes. Conference and Labs of the Evaluation Forums (CLEF 2016), 05-08 Sep 2016, Évora, Portugal. CEUR-WS.org , pp. 254-278.
Abstract
Since 2010, ImageCLEF has run a scalable image annotation task, to promote research into the annotation of images using noisy web page data. It aims to develop techniques to allow computers to describe images reliably, localise different concepts depicted and generate descriptions of the scenes. The primary goal of the challenge is to encourage creative ideas of using web page data to improve image annotation. Three sub-tasks and two pilot teaser tasks were available to participants; all tasks use a single mixed modality data source of 510,123 web page items for both training and test. The dataset included raw images, textual features obtained from the web pages on which the images appeared, as well as extracted visual features. Extracted from the Web by querying popular image search engines, the dataset was formed. For the main sub-tasks, the development and test sets were both taken from the “training set”. For the teaser tasks, 200,000 web page items were reserved for testing, and a separate development set was provided. The 251 concepts were chosen to be visual objects that are localizable and that are useful for generating textual descriptions of the visual content of images and were mined from the texts of our extensive database of image-webpage pairs. This year seven groups participated in the task, submitting over 50 runs across all sub-tasks, and all participants also provided working notes papers. In general, the groups’ performance is impressive across the tasks, and there are interesting insights into these very relevant challenges.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2016 The Authors and Editors. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council EP/K019082/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Aug 2016 15:41 |
Last Modified: | 19 Jun 2020 13:52 |
Published Version: | http://ceur-ws.org/Vol-1609/ |
Status: | Published |
Publisher: | CEUR-WS.org |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:103417 |