By Topic

Multiclass object classification for computer vision using Linear Genetic Programming

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Downey, C. ; Sch. of Eng. & Comput. Sci., Victoria Univ. of Wellington, Wellington, New Zealand ; Mengjie Zhang

Multiclass classification problems arise naturally in many tasks in computer vision; typical examples include image segmentation and letter recognition. These are among some of the most challenging and important tasks in the area and solutions to them are eagerly sought after. Genetic programming (GP) is a powerful and flexible machine learning technique that has been successfully applied to many binary classification tasks. However, the traditional form of GP performs poorly on multiclass classification problems. Linear GP (LGP) is an alternative form of GP where programs are represented as sequences of instructions like Java and C++. This paper discusses results which demonstrate the superiority of LGP as a technique for multiclass classification. It also discusses a new extension to LGP which results in a further improvement in the performance on multiclass classification problems.

Published in:

Image and Vision Computing New Zealand, 2009. IVCNZ '09. 24th International Conference

Date of Conference:

23-25 Nov. 2009