Learning classes for video interpretation with a robust parallel clustering method

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 infra-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 algorithm which can be applied in parallel to a (non-predefined) number of classes. Our clustering technique overcome three main limitations of standard K-means methods: sensitivity to initialization, choice of the number of clusters and influence of outliers. Moreover, it can process the training data in an incremental way. Experimental results on sports videos are reported.