Dual-Tree Genetic Programming for Few-Shot Image Classification

Abstract

Few-shot image classification (FSIC) is an important but challenging task due to high variations across images and a small number of training instances. A learning system often has poor generalization performance due to the lack of sufficient training data. Genetic programming (GP) has been successfully applied to image classification and achieved promising performance. This article proposes a GP-based approach with a dual-tree representation and a new fitness function to automatically learn image features for FSIC. The dual-tree representation allows the proposed approach to have better searchability and learn richer features than a single-tree representation when the number of training instances is very small. The fitness function based on the classification accuracy and the distances of the training instances to the class centroids aims to improve the generalization performance. The proposed approach can deal with different types of FSIC tasks with various numbers of classes and different image sizes. The results show that the proposed approach achieves significantly better performance than a large number of state-of-the-art methods on nine 3-shot and 5-shot image classification datasets. Further analysis shows the effectiveness of the new components of the proposed approach, its good searchability, and the high interpretability of the evolved solutions.