Advertisement

Evolutionary Visual Learning with Linear Genetic Programming

  • Gustavo Olague
Chapter
Part of the Natural Computing Series book series (NCS)

Abstract

This chapter presents a linear genetic programming approach that solves simultaneously the region selection and feature extraction tasks, which are applicable to common image recognition problems. The method searches for optimal regions of interest, using texture information as its feature space and classification accuracy as the fitness function. Texture is analyzed based on the gray level cooccurrence matrix and classification is carried out by an SVM committee. Results show effective performance compared with previous results using a standard image database.

Keywords

Feature Extraction Object Recognition Training Image Confusion Matrix Facial Expression Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Gustavo Olague
    • 1
  1. 1.EvoVisión Research TeamCICESE Research CenterEnsenadaMexico

Personalised recommendations