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A Gaussian Filter-Based Feature Learning Approach Using Genetic Programming to Image Classification

  • Ying BiEmail author
  • Bing Xue
  • Mengjie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)

Abstract

To learn image features automatically from the problems being tackled is more effective for classification. However, it is very difficult due to image variations and the high dimensionality of image data. This paper proposes a new feature learning approach based on Gaussian filters and genetic programming (GauGP) for image classification. Genetic programming (GP) is a well-known evolutionary learning technique and has been applied to many visual tasks, showing good learning ability and interpretability. In the proposed GauGP method, a new program structure, a new function set and a new terminal set are developed, which allow it to detect small regions from the input image and to learn discriminative features using Gaussian filters for image classification. The performance of GauGP is examined on six different data sets of varying difficulty and compared with four GP methods, eight traditional approaches and convolutional neural networks. The experimental results show GauGP achieves significantly better or similar performance in most cases.

Keywords

Feature learning Genetic programming Image classification Gaussian filter Evolutionary computation Feature extraction 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

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