Abstract
This paper presents the algorithms and results of our participation to the image annotation task of ImageCLEFmed 2007. We proposed a multi-cue approach where images are represented both by global and local descriptors. These cues are combined following two SVM-based strategies. The first algorithm, called Discriminative Accumulation Scheme (DAS), trains an SVM for each feature, and considers as output of each classifier the distance from the separating hyperplane. The final decision is taken on a linear combination of these distances. The second algorithm, that we call Multi Cue Kernel (MCK), uses a new Mercer kernel which can accept as input different features while keeping them separated. The DAS algorithm obtained a score of 29.9, which ranked fifth among all submissions. The MCK algorithm with the one-vs-all and with the one-vs-one multiclass extensions of SVM scored respectively 26.85 and 27.54. These runs ranked first and second among all submissions.
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References
Güld, M.O., Kohnen, M., Keysers, D., Schubert, H., Wein, B.B., Bredno, J., Lehmann, T.M.: Quality of dicom header information for image categorization. In: Proc of SPIE Medical Imaging, vol. 4685, pp. 280–287 (2002)
Müller, H., Deselaers, T., Kim, E., Kalpathy-Cramer, J., Deserno, T.M., Clough, P., Hersh, W.: Overview of the ImageCLEFmed 2007 medical retrieval and annotation tasks. In: Working Notes of the 2007 CLEF Workshop (2007)
Müller, H., Gass, T., Geissbuhler, A.: Performing image classification with a frequency-based information retrieval schema for ImageCLEF 2006. In: Working Notes of the 2006 CLEF Workshop (2006)
Liu, J., Hu, Y., Li, M., Ma, W.Y.: Medical image annotation and retrieval using visual features. In: Working Notes of the 2006 CLEF Workshop (2006)
Güld, M., Thies, C., Fischer, B., Lehmann, T.: Baseline results for the imageclef 2006 medical automatic annotation task. In: Working Notes of the 2006 CLEF Workshop (2006)
Nilsback, M.E., Caputo, B.: Cue integration through discriminative accumulation. In: Proc of CVPR (2004)
Lehmann, T.M., Henning, S., Daniel, K., Michael, K., Bethold Wein, B.: The irma code for unique classification of medical images. In: Proc of SPIE Medical Imaging, vol. 5033, pp. 440–451 (2003)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc of ICCV, vol. 2, pp. 1150–1157 (1999)
Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954. Springer, Heidelberg (2006)
Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grouping using the nyström method. PAMI 26(2), 214–225 (2004)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
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Tommasi, T., Orabona, F., Caputo, B. (2008). Cue Integration for Medical Image Annotation. In: Peters, C., et al. Advances in Multilingual and Multimodal Information Retrieval. CLEF 2007. Lecture Notes in Computer Science, vol 5152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85760-0_72
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DOI: https://doi.org/10.1007/978-3-540-85760-0_72
Publisher Name: Springer, Berlin, Heidelberg
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