Learning from uncertain image data using granular fuzzy sets and bio-mimetic applicability functions.
ABSTRACT In this paper we present a new method for ma- chine learning from images using uncertain informa- tion granules which has been inspired by bio-mimetic study of human perceptual processing. We present a method for generating labelled image data using the rapid application of rough labelled regions to the im- age under study. Over each region is defined an ap- plicability function which acts as a centre of focus for the uncertain information contained within the re- gion. We present a number of alternative applicabil- ity functions inspired by the human visual system and particularly by the centre-weighted effect of the fovea within the retina. We also show how this uncertain data can be used to directly train a granular fuzzy ma- chine learner.
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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.Fuzzy Sets and Systems 02/2007; 158(3):295-311. DOI:10.1016/j.fss.2006.10.012 · 1.88 Impact Factor