Bio-mimetic learning from images using imprecise expert information

Authors:
Jonathan Rossiter;Toshiharu Mukai
Affiliations:
Artificial Intelligence Research Group, Department of Engineering Mathematics, University of Bristol, Bristol BS8 1TR, UK;Bio-mimetic Control Research Center, RIKEN (The Institute of Physical and Chemical Research), 2271-130, Anagahora, Shimoshidami, Moriyama-ku, Nagoya 463-0003, Japan
Venue:
Fuzzy Sets and Systems
Year:
2007

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Abstract

We present a method for training a cross-product granular model with uncertain image data provided by domain experts. This image data is generated by a process of vague image tagging where experts label regions in the image using vague and general shapes. This is possible through a number of observations of, and assumptions about, human behaviour and the human visual system. We focus on the human tendency to concentrate on one central region of interest at a time and from this characteristic we define an applicability function across each tagged shape. We present bio-mimetic justification for our choice of applicability function and show examples of the vague tagging process and machine learning with this tagged data using a cross-product granule learner. Illustrated applications include medical decision making from radiological images and guided training of robots in hazardous environments.