Abstract
We present and discuss our participation in the four tasks of the ImageCLEF 2006 Evaluation. In particular, we present a novel approach to learn feature weights in our content-based image retrieval system FIRE. Given a set of training images with known relevance among each other, the retrieval task is reformulated as a classification task and then the weights to combine a set of features are trained discriminatively using the maximum entropy framework. Experimental results for the medical retrieval task show large improvements over heuristically chosen weights. Furthermore the maximum entropy approach is used for the automatic image annotation tasks in combination with a part-based object model. Using our object classification methods, we obtained the best results in the medical and in the object annotation task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Berger, A.L., Della Pietra, S.A., Della Pietra, V.J.: A maximum entropy approach to natural language processing. Computational Linguistics 22, 39–72 (1996)
Bender, O., Och, F., Ney, H.: Maximum entropy models for named entity recognition. In: 7th Conference on Computational Natural Language Learning, Edmonton, Canada, pp. 148–152 (2003)
Mauser, A., Bezrukov, I., Deselaers, T., Keysers, D.: Predicting customer behavior using naive bayes and maximum entropy – winning the data-mining-cup 2004. In: Informatiktage 2005 der Gesellschaft für Informatik, St. Augustin, Germany (Inpress, 2005)
Keysers, D., Och, F.J., Ney, H.: Maximum entropy and Gaussian models for image object recognition. In: Pattern Recognition, 24th DAGM Symposium, Zürich, Switzerland, pp. 498–506 (2002)
Lazebnik, S., Schmid, C., Ponce, J.: A maximum entropy framework for part-based texture and object recognition. In: IEEE International Conference on Computer Vision (ICCV 2005), Bejing, China, vol. 1, pp. 832–838 (2005)
Müller, H., Deselaers, T., Lehmann, T., Clough, P., Hersh, W.: Overview of the imageclefmed, medical retrieval and annotation tasks. In: Evaluation of Multilingual and Multi-modal Information Retrieval – Seventh Workshop of the Cross-Language Evaluation Forum, CLEF 2006. LNCS, Alicante, Spain (to appear, 2007)
Clough, P., Grubinger, M., Deselaers, T., Hanbury, A., Müller, H.: Overview of the imageclef, photographic retrieval and object annotation tasks. In: Evaluation of Multilingual and Multi-modal Information Retrieval – Seventh Workshop of the Cross-Language Evaluation Forum, CLEF 2006. LNCS, Alicante, Spain (to appear, 2007)
Keysers, D., Och, F.J., Ney, H.: Efficient maximum entropy training for statistical object recognition. In: Informatiktage 2002 der Gesellschaft für Informatik, Bad Schussenried, Germany pp. 342–345 (2002)
Grubinger, M., Clough, P., Müller, H., Deselaers, T.: The iapr benchmark: A new evaluation resource for visual information systems. In: LREC 2006 OntoImage 2006: Language Resources for Content-Based Image Retrieval, Genoa, Italy (in press, 2006)
Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval – a quantitative comparison. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) Pattern Recognition. LNCS, vol. 3175, pp. 228–236. Springer, Heidelberg (2004)
Keysers, D., Gollan, C., Ney, H.: Local context in non-linear deformation models for handwritten character recognition. In: International Conference on Pattern Recognition, Cambridge, UK, vol. 4, pp. 511–514 (2004)
Keysers, D., Gollan, C., Ney, H.: Classification of medical images using non-linear distortion models. In: Proc. BVM 2004, Bildverarbeitung für die Medizin, Berlin, Germany, pp. 366–370 (2004)
Clough, P., Mueller, H., Deselaers, T., Grubinger, M., Lehmann, T., Jensen, J., Hersh, W.: The clef 2005 cross-language image retrieval track. In: Peters, C., Gey, F.C., Gonzalo, J., Müller, H., Jones, G.J.F., Kluck, M., Magnini, B., de Rijke, M., Giampiccolo, D. (eds.) CLEF 2005. LNCS, vol. 4022, pp. 535–557. Springer, Heidelberg (2006)
Deselaers, T., Keysers, D., Ney, H.: Discriminative training for object recognition using image patches. In: IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, vol. 2, pp. 157–162 (2005)
Deselaers, T., Hegerath, A., Keysers, D., Ney, H.: Sparse patch-histograms for object classification in cluttered images. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) Pattern Recognition. LNCS, vol. 4174, pp. 202–211. Springer, Heidelberg (2006)
Dorkó, G., Schmid, C.: Object class recognition using discriminative local features. IEEE Transactions on Pattern Analysis and Machine Intelligence (submitted 2004)
Deselaers, T., Weyand, T., Keysers, D., Macherey, W., Ney, H.: FIRE in ImageCLEF 2005: Combining content-based image retrieval with textual information retrieval. In: Peters, C., Gey, F.C., Gonzalo, J., Müller, H., Jones, G.J.F., Kluck, M., Magnini, B., de Rijke, M., Giampiccolo, D. (eds.) CLEF 2005. LNCS, vol. 4022, Springer, Heidelberg (2006)
Hegerath, A., Deselaers, T., Ney, H.: Patch-based object recognition using discriminatively trained gaussian mixtures. In: 17th British Machine Vision Conference (BMVC 2006), Edinburgh, UK, vol. 2, pp. 519–528 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Deselaers, T., Weyand, T., Ney, H. (2007). Image Retrieval and Annotation Using Maximum Entropy. In: Peters, C., et al. Evaluation of Multilingual and Multi-modal Information Retrieval. CLEF 2006. Lecture Notes in Computer Science, vol 4730. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74999-8_91
Download citation
DOI: https://doi.org/10.1007/978-3-540-74999-8_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74998-1
Online ISBN: 978-3-540-74999-8
eBook Packages: Computer ScienceComputer Science (R0)