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A Neural Network to Retrieve Images from Text Queries

  • David Grangier
  • Samy Bengio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

This work presents a neural network for the retrieval of images from text queries. The proposed network is composed of two main modules: the first one extracts a global picture representation from local block descriptors while the second one aims at solving the retrieval problem from the extracted representation. Both modules are trained jointly to minimize a loss related to the retrieval performance. This approach is shown to be advantageous when compared to previous models relying on unsupervised feature extraction: average precision over Corel queries reaches 26.2% for our model, which should be compared to 21.6% for PAMIR, the best alternative.

Keywords

Image Retrieval Local Binary Pattern Average Precision Retrieval Model Probabilistic Latent Semantic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D.M., Jordan, M.I.: Matching words and pictures. J. of Machine Learning Research 3 (2003)Google Scholar
  2. 2.
    Grangier, D., Bengio, S.: A discriminative approach for the retrieval of images from text queries. Technical report, IDIAP Research Institute (2006)Google Scholar
  3. 3.
    Jeon, J., Lavrenko, V., Manmatha, R.: Automatic image annotation and retrieval using cross-media relevance models. In: ACM Special Interest Group on Information Retrieval (2003)Google Scholar
  4. 4.
    Monay, F., Gatica-Perez, D.: PLSA-based image auto-annotation: constraining the latent space. In: ACM Multimedia (2004)Google Scholar
  5. 5.
    Duygulu, P., Barnard, K., de Freitas, N., Forsyth, D.: Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In: European Conf. on Computer Vision (2002)Google Scholar
  6. 6.
    Tieu, K., Viola, P.: Boosting image retrieval. Intl. J. of Computer Vision 56 (2004)Google Scholar
  7. 7.
    Wu, H., LuE, H., Ma, S.: A practical SVM-based algorithm for ordinal regression in image retrieval. In: ACM Multimedia (2003)Google Scholar
  8. 8.
    LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Handwritten digit recognition with a back-propagation network. In: Conf. on Advances in Neural Information Processing Systems (1989)Google Scholar
  9. 9.
    Garcia, C., Delakis, M.: Convolutional face finder: A neural architecture for fast and robust face detection. T. on Pattern Analysis and Machine Intelligence 26 (2004)Google Scholar
  10. 10.
    Joachims, T.: Optimizing search engines using clickthrough data. In: Intl. Conf. on Knowledge Discovery and Data Mining (2002)Google Scholar
  11. 11.
    Grangier, D., Bengio, S.: Exploiting hyperlinks to learn a retrieval model. In: NIPS Workshop on Learning to Rank (2005)Google Scholar
  12. 12.
    Quelhas, P., Monay, F., Odobez, J.M., Gatica-Perez, D., Tuytelaars, T., Gool, L.J.V.: Modeling scenes with local descriptors and latent aspects. In: Intl. Conf. on Computer Vision (2005)Google Scholar
  13. 13.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley, Harlow (1999)Google Scholar
  14. 14.
    LeCun, Y., Bottou, L., Orr, G.B., Mueller, K.R.: Efficient backprop. In: Orr, G.B., Mueller, K.R. (eds.) Neural Networks: Trick of the Trade, Springer, Heidelberg (1998)Google Scholar
  15. 15.
    Takala, V., Ahonen, T., Pietikainen, M.: Block-based methods for image retrieval using local binary patterns. In: Scandinavian Conf. on Image Analysis (2005)Google Scholar
  16. 16.
    Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Intl. Conf. on Machine Learning (2005)Google Scholar
  17. 17.
    Rice, J.: Rice, Mathematical Statistics and Data Analysis. Duxbury Press, Belmont (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • David Grangier
    • 1
    • 2
  • Samy Bengio
    • 1
  1. 1.IDIAP Research InstituteMartignySwitzerland
  2. 2.Ecole Polytechnique Fédérale de Lausanne (EPFL)Switzerland

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