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2009
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Evolving novel image features using Genetic Programming-based image transforms

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Evolving novel image features using Genetic Programming-based image transforms
— In this paper, we use Genetic Programming (GP) to define a set of transforms on the space of greyscale 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 GPtransformed 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 OPMD-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.
Taras Kowaliw, Wolfgang Banzhaf, Nawwaf N. Kharma,
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where CEC
Authors Taras Kowaliw, Wolfgang Banzhaf, Nawwaf N. Kharma, Simon Harding
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