T. Kowaliw, W. Banzhaf, N. Kharma, and S. Harding
at IEEE Congress on Evolutionary Computation CEC'09
paper preprint (PDF)
definitive version
BibTEX
For an introductory text, see the TEF project page.
In this paper, we use Genetic Programming (GP) to define a set of transforms on the space of grayscale images. The motivation is to allow an evolutionary algorithm means of transforming a set of image patterns into a more classifiable form. To this end, we introduce the notion of a Transform-based Evolvable Feature (TEF), a moment value extracted from a GP-transformed image, used in a classification task. Unlike many previous approaches, the TEF allows the whole image space to be searched and augmented. TEFs are instantiated through Cartesian Genetic Programming, and applied to a medical image classification task, that of detecting muscular dystrophy-indicating inclusions in cell images. It is shown that the inclusion of a single TEF allows for significantly superior classification relative to predefined features alone.