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A gaussian filter-based feature learning approach using genetic programming to image classification

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conference contribution
posted on 11.02.2021, 02:27 by Ying Bi, Bing Xue, Mengjie Zhang
© Springer Nature Switzerland AG 2018. 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.

History

Preferred citation

Bi, Y., Xue, B. & Zhang, M. (2018, January). A gaussian filter-based feature learning approach using genetic programming to image classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (11320 LNAI pp. 251-257). Springer International Publishing. https://doi.org/10.1007/978-3-030-03991-2_25

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

11320 LNAI

Publication or Presentation Year

01/01/2018

Pagination

251-257

Publisher

Springer International Publishing

Publication status

Published

ISSN

0302-9743

eISSN

1611-3349