An Automatic Feature Extraction Approach toImage Classification Using GeneticProgramming.pdf (4.4 MB)

An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

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conference contribution
posted on 29.10.2020, 00:43 by Ying Bi, Bing Xue, Mengjie Zhang
© Springer International Publishing AG, part of Springer Nature 2018. Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.

History

Preferred citation

Bi, Y., Xue, B. & Zhang, M. (2018, January). An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 21st International Conference on the Applications of Evolutionary Computation (EvoApplications), Parma, ITALY (10784 LNCS pp. 421-438). Springer International Publishing. https://doi.org/10.1007/978-3-319-77538-8_29

Conference name

21st International Conference on the Applications of Evolutionary Computation (EvoApplications)

Conference Place

Parma, ITALY

Conference start date

04/04/2018

Conference finish date

06/04/2018

Title of proceedings

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

Volume

10784 LNCS

Series

Lecture Notes in Computer Science

Publication or Presentation Year

01/01/2018

Pagination

421-438

Publisher

Springer International Publishing

Publication status

Published

ISSN

0302-9743

eISSN

1611-3349

Exports