NDLI: On Human-Machine Interaction during Online Image Classifier Training

Content Provider	IEEE Xplore Digital Library
Author	Lughofer, E. Smith, J. Caleb-Solly, P. Tahir, M.A. Eitzinger, C. Sannen, D. Van Brussel, H.
Copyright Year	2008
Description	Author affiliation: Dept. of Knowledge-based Math. Syst., Johannes Kepler Univ., Linz, Austria (Lughofer, E.)
Abstract	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.
Starting Page	1005
Ending Page	1010
File Size	318487
Page Count	6
File Format	PDF
ISBN	9780769535142
DOI	10.1109/CIMCA.2008.45
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-10
Publisher Place	Austria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Humans Inspection Application software on-line adaptivity Surface treatment Machine vision human-machine interaction Machine learning Production Man machine systems Manufacturing Image classification
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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