Skip to main content

Typed Cartesian Genetic Programming for Image Classification

Cattani, Phil T., Johnson, Colin G. (2009) Typed Cartesian Genetic Programming for Image Classification. In: Proceedings of the 2009 UK Workshop on Computational Intelligence. . pp. 182-196. , University of Nottingham (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) (KAR id:30597)

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. (Contact us about this Publication)
Official URL
http://www.cs.kent.ac.uk/pubs/2009/2971

Abstract

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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Colin Johnson
Date Deposited: 21 Sep 2012 09:49 UTC
Last Modified: 16 Feb 2021 12:41 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/30597 (The current URI for this page, for reference purposes)
Johnson, Colin G.: https://orcid.org/0000-0002-9236-6581
  • Depositors only (login required):