On Human-Machine Interaction During Online Image Classifier Training
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA) 2008
Original Kurzfassung:
This paper considers a number of issues that arise when a
trainable machine vision system learns directly from humans,
rather than from a ``cleaned'' data set, i.e.\ data items which are
perfectly labelled with complete accuracy. This is done within the
context of a generic system for the visual surface inspection of
manufactured parts. The issues treated are relevant not
only to wider computer vision applications, but also to classification more generally. Some of
these issues 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, online
adaptation in response to users' input. 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 real data sets from a CD imprint production process,
we will present results which show that properly addressing most
of these issues may actually lead to improved performance.
Forschungs-