Typed Cartesian Genetic Programming for Image Classification

Cattani, Phil T. and Johnson, Colin G. (2009) Typed Cartesian Genetic Programming for Image Classification. In: Proceedings of the 2009 UK Workshop on Computational Intelligence. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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This paper introduces an extension to Cartesian Genetic Programming (CGP), aimed at image classification problems. Individuals in the population consist of two layers of functions: image processing functions, and traditional mathematical functions. Information can be passed between these layers, and the final result can either be an image or a numerical value. This has been applied to image classification, by using CGP to evolve image processing algorithms for feature extraction. This paper presents results which show that these automatically extracted features can substantially increase classification accuracy on a medical problem concerned with the analysis of potentially cancerous cells.

Item Type: Conference or workshop item (UNSPECIFIED)
Uncontrolled keywords: determinacy analysis, Craig interpolants
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Computational Intelligence Group
Depositing User: Colin Johnson
Date Deposited: 21 Sep 2012 09:49
Last Modified: 21 Sep 2012 09:49
Resource URI: https://kar.kent.ac.uk/id/eprint/30597 (The current URI for this page, for reference purposes)
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