An Effective Feature Learning Approach Using Genetic Programming with Image Descriptors forImage Classification.pdf (1.05 MB)

An effective feature learning approach using genetic programming with image descriptors for image classification

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journal contribution
posted on 29.10.2020, 01:04 by Ying Bi, Bing Xue, Mengjie Zhang
© 2005-2012 IEEE. Being able to extract effective features from different images is very important for image classification, but it is challenging due to high variations across images. By integrating existing well-developed feature descriptors into learning algorithms, it is possible to automatically extract informative high-level features for image classification. As a learning algorithm with a flexible representation and good global search ability, genetic programming can achieve this. In this paper, a new genetic programming-based feature learning approach is developed to automatically select and combine five existing well-developed descriptors to extract high-level features for image classification. The new approach can automatically learn various numbers of global and/or local features from different types of images. The results show that the new approach achieves significantly better classification performance in almost all the comparisons on eight data sets of varying difficulty. Further analysis reveals the effectiveness of the new approach to finding the most effective feature descriptors or combinations of them to extract discriminative features for different classification tasks.

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Preferred citation

Bi, Y., Xue, B. & Zhang, M. (2020). An effective feature learning approach using genetic programming with image descriptors for image classification. IEEE Computational Intelligence Magazine, 15(2), 65-77. https://doi.org/10.1109/MCI.2020.2976186

Journal title

IEEE Computational Intelligence Magazine

Volume

15

Issue

2

Publication date

01/05/2020

Pagination

65-77

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Publication status

Published

ISSN

1556-603X

eISSN

1556-6048

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