Abstract:We propose an original learning approach for image classification problems. Recognizing semantic events in video requires to preliminary learn the different classes of events. This first stage is crucial since it conditions the further classification results. In video content analysis, the task is especially difficult due to the high intra-class variability and to noisy measurements. We then represent each class by the centers of several sub-classes (or clusters) thanks to a robust partitional clustering algor… Show more
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