Few-shot image classification method based on hierarchical learning genetic programming algorithm

Abstract

Disclosed is a few-shot image classification method based on a hierarchical learning genetic programming algorithm: constructing a few-shot image classification system based on the hierarchical learning genetic programming algorithm; dividing the image data set into a training set and a test set; performing image preprocessing and feature extraction operations on the training set to construct a feature storage table; constructing an ensemble solution by using features in the feature storage table as terminal inputs, and optimizing a final classification effect by using an ensemble strategy based on an individual difference value; using the test set as an input of an image classification solution, outputting predicted class labels of the test set, and evaluating a performance of the image classification solution according to an actual label of the test set; using to-be-classified image data as the input of the image classification solution, and outputting an image classification result.