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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®erent types of information. These cues are combined during the classi¯cation step following two alternative SVM-based strategies. The ¯rst algorithm, called Discriminative Accumulation Scheme (DAS), trains an SVM for each feature type, and considers as output of each classi¯er the distance from the separating hyper- plane. The ¯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 ¯nal result can be correct. The second algorithm uses a new Mercer kernel that can accept as input di®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°Or-tOMed.im2) from the name of our sponsors. The DAS algorithm obtained a score of 29.9, which ranked ¯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 ¯rst and second among all submissions.

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