Evaluating cluster quality for visual data
Digital video cameras have made it easy to collect large amounts of unlabeled data that can be used to learn to recognize objects and actions. Collecting ground-truth labels for this data, however, is a much more time consuming task that requires human intervention. One approach to train on this data, while keeping the human workload to a minimum, is to cluster the unlabeled samples, evaluate the quality of the clusters, and then ask a human annotator to label only the clusters believed to be dominated by a single object/action class. This thesis addresses the task of evaluating the quality of ...
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