Summary of the paper

Title News Image Annotation on a Large Parallel Text-image Corpus
Authors Pierre Tirilly, Vincent Claveau and 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.
Topics Multimedia Document Processing, Corpus (creation, annotation, etc.), Information Extraction, Information Retrieval
Full paper News Image Annotation on a Large Parallel Text-image Corpus
Slides News Image Annotation on a Large Parallel Text-image Corpus
Bibtex @InProceedings{TIRILLY10.772,
  author = {Pierre Tirilly and Vincent Claveau and Patrick Gros},
  title = {News Image Annotation on a Large Parallel Text-image Corpus},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  month = {may},
  date = {19-21},
  address = {Valletta, Malta},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-6-7},
  language = {english}
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