Inter-active learning of ad-hoc classifiers for video visual analytics

Autor(en): Höferlin, B.
Netzel, R.
Höferlin, M.
Weiskopf, D.
Heidemann, G. 
Stichwörter: Active Learning; Analytical reasoning; Background knowledge; Classifier models; Computing methodologies; Graphical user interfaces; H.3.3 [Information Systems]: Information Storage and Retrieval - Information Search and Retrieval; Human expert; I.2.6 [Computing Methodologies]: Artificial Intelligence - Learning; Information storage and retrieval; Mental model; Model visualization; Random sampling; Time constraints; Training process; Training sets; Visual analytics; Visual communication; Visual feedback, Artificial intelligence; Visualization, Active filters
Erscheinungsdatum: 2012
Journal: IEEE Conference on Visual Analytics Science and Technology 2012, VAST 2012 - Proceedings
Startseite: 23
Seitenende: 32
Learning of classifiers to be used as filters within the analytical reasoning process leads to new and aggravates existing challenges. Such classifiers are typically trained ad-hoc, with tight time constraints that affect the amount and the quality of annotation data and, thus, also the users' trust in the classifier trained. We approach the challenges of ad-hoc training by inter-active learning, which extends active learning by integrating human experts' background knowledge to greater extent. In contrast to active learning, not only does inter-active learning include the users' expertise by posing queries of data instances for labeling, but it also supports the users in comprehending the classifier model by visualization. Besides the annotation of manually or automatically selected data instances, users are empowered to directly adjust complex classifier models. Therefore, our model visualization facilitates the detection and correction of inconsistencies between the classifier model trained by examples and the user's mental model of the class definition. Visual feedback of the training process helps the users assess the performance of the classifier and, thus, build up trust in the filter created. We demonstrate the capabilities of inter-active learning in the domain of video visual analytics and compare its performance with the results of random sampling and uncertainty sampling of training sets. © 2012 IEEE.
Conference of 2012 IEEE Conference on Visual Analytics Science and Technology, VAST 2012 ; Conference Date: 14 October 2012 Through 19 October 2012; Conference Code:95202
ISBN: 9781467347532
DOI: 10.1109/VAST.2012.6400492
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