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.
|Translated title of the contribution||Bio-mimetic learning from images using imprecise expert information|
|Pages (from-to)||295 - 311|
|Number of pages||17|
|Journal||Fuzzy Sets and Systems|
|Publication status||Published - Feb 2007|