Bio-mimetic learning from images using imprecise expert information

JM Rossiter, T Mukai

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)

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.
Translated title of the contributionBio-mimetic learning from images using imprecise expert information
Original languageEnglish
Pages (from-to)295 - 311
Number of pages17
JournalFuzzy Sets and Systems
Volume158 (3)
DOIs
Publication statusPublished - Feb 2007

Bibliographical note

Publisher: Elsevier

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