CLEF2007: Image Annotation Task: an SVM-based Cue Integration Approach

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 descriptors, so to capture different types of information. These cues are combined during the classification step following two alternative SVM-based strategies. The first algorithm, called Discriminative Accumulation Scheme (DAS), trains an SVM for each feature type, 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: in this way cues are accumulated, thus even when they both are misleaded the final result can be correct. The second algorithm uses a new Mercer kernel that can accept as input different 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 (BLanceflOr-tOMed.im2) from the name of our sponsors. The DAS algorithm obtained a score of 29.9, which ranked fifth 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 first and second among all submissions.