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

A few-shot image classification method based on a hierarchical learning genetic programming algorithm, comprising the following steps:

• step 1:
• constructing a few-shot image classification system based on the hierarchical learning genetic programming algorithm, wherein the few-shot image classification system is provided with an image acquisition module, the image acquisition module is connected with a hierarchical evolutionary learning framework, and the hierarchical evolutionary learning framework is provided with a parallel exploration genetic programming (PEGP) module on a first layer and a development-ensemble genetic programming (DEGP) module on a second layer;
• the PEGP module is provided with three different types of feature exploration blocks in parallel, the three feature exploration blocks are respectively a first feature exploration block, a second feature exploration block, and a third feature exploration block, program structures, function sets and terminal sets of the first feature exploration block, the second feature exploration block and the third feature exploration block are respectively set, and the first feature exploration block, the second feature exploration block and the third feature exploration block generate three feature construction blocks with different evolutionary directions in parallel;
• step 2:
• acquiring an image data set, and dividing the image data set into a training set and a test set by the image acquisition module;
• step 3:
• acquiring training set data, and performing image preprocessing and feature extraction operations on the training set data to construct a feature storage table, and then transferring the feature storage table to the DEGP module by the PEGP module;
• step 4:
• further constructing an ensemble solution by using features in the feature storage table as terminal inputs, and finally optimizing a final classification effect by using an ensemble strategy based on an individual difference value by the DEGP module, thereby outputting a high-performance image classification solution;
• step 5:
• using the test set as an input of the image classification solution, then, outputting predicted class labels of the test set, and finally, evaluating a performance of the image classification solution according to an actual label of the test set;
• and
• step 6:
• acquiring to-be-classified image data, using the to-be-classified image data as the input of the image classification solution, and outputting an image classification result by the image acquisition module.