Learn More
Classification of detected events is a central component in state-of-the-art surface inspection systems that still relies on manual parametrization. While machine-learned classifiers promise supreme accuracy, their reliability depends on complete and correct annotation of an extensive training database, leaving the risk of unpredictable behavior in changing(More)
Active learning facilitates the training of classifiers by selectively querying the user in order to gain insights on unlabeled data samples. Until recently, the user had limited abilities to interact with an active learning system: A sub-selection was presented by the system and every sample within had to be annotated. We propose an alternative and(More)
Drift detection is an important issue in classification-based stream mining in order to be able to inform the operators in case of unintended changes in the system. Usually, current detection approaches rely on the assumption to have fully supervised labeled streams available, which is often a quite unrealistic scenario in on-line real-world applications.(More)
In this paper, we are dealing with the automatic inclusion of new event types in visual inspection systems. Within the context of image classification for recognizing "OK" and "not OK" parts, a certain event can be directly associated with a class, as events are usually independent and disjoint from each other. In this sense, we are dealing with the problem(More)
  • 1