# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 1 Introduction  
Image classification aims to analyze image content and assign the correct category labels and is one of the core tasks in the field of computer vision. This technology has been widely applied in many fields, such as image retrieval, face recognition, autonomous driving, and medical image analysis [1; 2; 3; 4]. Adequate and high-quality training samples ensure the precise capture of key features to complete the classification task. However, in the real world, most of the images obtained are of low quality, often containing a considerable amount of blurry information and noise. Therefore, analyzing degraded images and performing accurate classification is a more challenging task. Extracting effective features is crucial for image classification tasks. Classic manual feature extraction methods such as Local Binary Patterns (LBP) [5], Scale-Invariant Feature Transform (SIFT) [6], and Histogram of Oriented Gradients (HOG) [7] aim to capture key information features of images, thereby achieving efficient image classification. However, despite these manual feature extraction methods demonstrating excellent performance on specific datasets, their classification performance may not be guaranteed when facing unknown types of classification tasks. Compared with manual feature extraction methods, feature learning approaches are usually more effective for image classification. This is because they can automatically extract distinctive features from raw images. Convolutional Neural Networks (CNNs), as the current mainstream method for feature learning, exhibit outstanding performance in many tasks. However, their performance is dependent on the expert's model design. Additionally, understanding and interpreting the decision-making process of CNNs remains a challenge. As a type of evolutionary algorithm, Genetic Programming (GP) [8] has attracted widespread attention due to its excellent learning performance and good interpretability. It is applied in various tasks, such as workshop scheduling [9], symbolic regression [10], and image classification [11]. In GP-based image classification methods, a tree representation structure is generally used. In tree-structured GP, the original image and the parameters required by internal nodes are referred to as leaf nodes or terminals, serving as inputs to the program. Image filters, feature descriptors, and other image processing-related operators are used as internal nodes/functions in tree-structured GP [12]. According to this definition, each individual generated by the GP program can be viewed as a potential solution for the classification task. Currently, image classification methods based on GP have made significant progress. Some approaches directly use raw images as inputs to the GP program. A common strategy among these is for the GP program to output learned high-level features, which are then fed into a classification algorithm for classification processing [13; 14]. Another approach integrates a variety of classifiers within the internal nodes of the GP program. This allows the GP to automatically choose the most suitable classifiers and produce the predicted classification results [15]. However, the aforementioned GP methods primarily focus on processing limited types and higher quality samples, and have not thoroughly explored the issue of degraded image classification. Although some studies have proposed the EFLGP method [16] aimed at degraded image tasks, limitations in its program structure and internal node design may lead to poor classification performance. Furthermore, a common issue in the aforementioned GP methods is the excessive homogeneity of individuals within the population during the middle and later stages of the GP algorithm, which can result in a stagnation in fitness improvement for the best GP individual/program. On the other hand, existing research typically selects the best individual as the solution for classification tasks, a method that easily leads to the occurrence of overfitting.