Human-Machine Interaction Issues in Quality Control Based on On-Line Image Classification
Sprache des Titels:
Englisch
Original Kurzfassung:
This paper considers on a number of issues that arise when a
trainable machine vision system learns directly from humans. We contrast this to the ``normal'' situation where Machine Learning techniques are applied to a ``cleaned'' data set which is considered perfectly labelled with complete accuracy. This study is done within the context of a generic system for the visual surface inspection of manufactured parts, however, the issues treated are relevant not
only to wider computer vision applications such as medical image screening, but also to classification more generally. Many of the issues we consider arise from the nature of humans themselves: they will be not only internally inconsistent, but will often not be
completely confident about their decisions, especially if they are making decisions rapidly. People will also often differ systematically from each other in the decisions they make. Other issues may arise from the nature of the process, which may require the machine learning to have the capacity for real-time, on-line adaptation in response to users' input. Because of this, it may be that the users
cannot always provide input to a consistent level of detail. We describe how all of these issues may be tackled within a coherent methodology. Using a range of classifiers trained on data sets from a CD imprint production process,
we present results which demonstrate that training methods designed to take proper consideration
of these issues may actually lead to improved performance.
Sprache der Kurzfassung:
Englisch
Journal:
IEEE Transaction on Systems, Man and Cybernetics part A: Systems and Humans