News Image Annotation on a Large Parallel Text-image Corpus

Pierre Tirilly, Vincent Claveau, Patrick Gros

Abstract
In this paper, we present a multimodal parallel text-image corpus, and propose an image annotation method that exploits the textual information associated with images. Our corpus contains news articles composed of a text, images and image captions, and is significantly larger than the other news corpora proposed in image annotation papers (27,041 articles and 42,568 captionned images). In our experiments, we use the text of the articles as a textual information source to annotate images, and image captions as a groundtruth to evaluate our annotation algorithm. Our annotation method identifies relevant named entities in the texts, and associates them with high-level visual concepts detected in the images (in this paper, faces and logos). The named entities most suited to image annotation are selected using an unsupervised score based on their statistics, inspired from the weights used in information retrieval. Our experiments show that, although it is very simple, our annotation method achieves an acceptable accuracy on our real-world news corpus.
Anthology ID:
L10-1532
Volume:
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
Month:
May
Year:
2010
Address:
Valletta, Malta
Editors:
Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, Daniel Tapias
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
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Pages:
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/772_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Pierre Tirilly, Vincent Claveau, and Patrick Gros. 2010. News Image Annotation on a Large Parallel Text-image Corpus. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
Cite (Informal):
News Image Annotation on a Large Parallel Text-image Corpus (Tirilly et al., LREC 2010)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2010/pdf/772_Paper.pdf
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