lensen2015hybrid.pdf (1.07 MB)

A hybrid Genetic Programming approach to feature detection and image classification

Download (1.07 MB)
conference contribution
posted on 06.10.2020 by Andrew Lensen, Harith Al-Sahaf, Mengjie Zhang, Bing Xue
© 2015 IEEE. Image classification is a crucial task in Computer Vision. Feature detection represents a key component of the image classification process, which aims at detecting a set of important features that have the potential to facilitate the classification task. In this paper, we propose a Genetic Programming (GP) approach to image feature detection. The proposed method uses the Speeded Up Robust Features (SURF) method to extract features from regions automatically selected by GP, and adopts a wrapper approach combined with a voting scheme to perform image classification. The proposed approach is evaluated using three datasets of increasing difficulty, and is compared to five popularly used machine learning methods: Support Vector Machines, Random Forest, Naive Bayes, Decision Trees, and Adaptive Boosting. The experimental results show the proposed approach has achieved comparable or better performance than the five existing methods on all three datasets, and reveal its capability to automatically detect good regions from a large image from which good features are automatically constructed.

History

Preferred citation

Lensen, A., Al-Sahaf, H., Zhang, M. & Xue, B. (2016, January). A hybrid Genetic Programming approach to feature detection and image classification. In International Conference Image and Vision Computing New Zealand Image and Vision Computing New Zealand (IVCNZ), 2015 International Conference on, Auckland, NZ (2016-November pp. 1-6). IEEE. https://doi.org/10.1109/IVCNZ.2015.7761564

Conference name

Image and Vision Computing New Zealand (IVCNZ), 2015 International Conference on

Conference Place

Auckland, NZ

Conference start date

23/11/2015

Conference finish date

24/11/2015

Title of proceedings

International Conference Image and Vision Computing New Zealand

Volume

2016-November

Series

International Conference on Image and Vision Computing New Zealand

Contribution type

Published Paper

Publication or Presentation Year

01/01/2016

Pagination

1-6

Publisher

IEEE

Publication status

Published

ISSN

2151-2191

eISSN

2151-2205

Exports