Hierarchical feature construction for image classification using Genetic Programming
Masanori Suganuma, Tsuchiya Daiki, Shinichi Shirakawa, Tomoharu Nagao · 2016
In this paper, we design a hierarchical feature construction method for image classification. Our method has two feature construction stages: (1) feature construction by a combination of primitive image processing filters, and (2) feature construction by evolved filters. We verify the image classification performance of the proposed method on the MIT urban and nature scene dataset. The experimental results show that the two-stage feature construction improves the classification accuracy compared to single stage feature construction. In addition, the proposed method outperforms several existing feature construction methods.