%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Tommasi_CLEF2007_2007,
         author = {Tommasi, Tatiana and Orabona, Francesco and Caputo, Barbara},
       projects = {Idiap, EMMA},
          title = {CLEF2007 Image Annotation Task: an SVM-based Cue Integration Approach},
      booktitle = {Proceedings of ImageCLEF 2007 -LNCS},
           year = {2007},
       abstract = {This paper presents the algorithms and results of our participation to the medical
image annotation task of ImageCLEFmed 2007. We proposed, as a general strategy,
a multi-cue approach where images are represented both by global and local descrip-
tors, so to capture di{\^{A}}{\textregistered}erent types of information. These cues are combined during the
classi{\^{A}}¯cation step following two alternative SVM-based strategies. The {\^{A}}¯rst algorithm,
called Discriminative Accumulation Scheme (DAS,',','),
 trains an SVM for each feature
type, and considers as output of each classi{\^{A}}¯er the distance from the separating hyper-
plane. The {\^{A}}¯nal decision is taken on a linear combination of these distances: in this
way cues are accumulated, thus even when they both are misleaded the {\^{A}}¯nal result can
be correct. The second algorithm uses a new Mercer kernel that can accept as input
di{\^{A}}{\textregistered}erent feature types while keeping them separated. In this way, cues are selected
and weighted, for each class, in a statistically optimal fashion. We call this approach
Multi Cue Kernel (MCK). We submitted several runs, testing the performance of the
single-cue SVM and of the two cue integration methods. Our team was called BLOOM
(BLance{\^{A}}°Or-tOMed.im2) from the name of our sponsors. The DAS algorithm obtained
a score of 29.9, which ranked {\^{A}}¯fth among all submissions. We submitted two versions
of the MCK algorithm, one using the one-vs-all multiclass extension of SVMs and
the other using the one-vs-one extension. They scored respectively 26.85 and 27.54,
ranking {\^{A}}¯rst and second among all submissions.},
            pdf = {https://publications.idiap.ch/attachments/papers/2008/Tommasi_CLEF2007_2007.pdf}
}