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Genetic programming for region detection, feature extraction, feature construction and classification in image data

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posted on 06.10.2020 by Andrew Lensen, Harith Al-Sahaf, Mengjie Zhang, Bing Xue
© Springer International Publishing Switzerland 2016. Image analysis is a key area in the computer vision domain that has many applications. Genetic Programming (GP) has been successfully applied to this area extensively, with promising results. Highlevel features extracted from methods such as Speeded Up Robust Features (SURF) and Histogram of Oriented Gradients (HoG) are commonly used for object detection with machine learning techniques. However, GP techniques are not often used with these methods, despite being applied extensively to image analysis problems. Combining the training process of GP with the powerful features extracted by SURF or HoG has the potential to improve the performance by generating high-level, domain-tailored features. This paper proposes a new GP method that automatically detects different regions of an image, extracts HoG features from those regions, and simultaneously evolves a classifier for image classification. By extending an existing GP region selection approach to incorporate the HoG algorithm, we present a novel way of using high-level features with GP for image classification. The ability of GP to explore a large search space in an efficient manner allows all stages of the new method to be optimised simultaneously, unlike in existing approaches. The new approach is applied across a range of datasets, with promising results when compared to a variety of well-known machine learning techniques. Some high-performing GP individuals are analysed to give insight into how GP can effectively be used with high-level features for image classification.

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Preferred citation

Lensen, A., Al-Sahaf, H., Zhang, M. & Xue, B. (2016, January). Genetic programming for region detection, feature extraction, feature construction and classification in image data. In Genetic Programming. EuroGP 2016. Lecture Notes in Computer Science (9594 pp. 51-67). Springer. https://doi.org/10.1007/978-3-319-30668-1_4

Title of proceedings

Genetic Programming. EuroGP 2016. Lecture Notes in Computer Science

Volume

9594

Contribution type

Published Paper

Publication or Presentation Year

01/01/2016

Pagination

51-67

Publisher

Springer

Publication status

Published

ISSN

0302-9743

eISSN

1611-3349

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