# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 2 Related Work
### Image Classification Methods
#### 2.3.3 GP-Based Image Classification Methods  
Tree-based GP has been successfully and widely applied in image classification. This method predefines certain operators and, during runtime, combines these operators within the problem space through an evolutionary algorithm to learn the best solution or program. This means that GP does not require extensive domain knowledge to adjust the model or tune parameters to obtain solutions for classification tasks, and these solutions often have a high level of interpretability. In current research on GP-based image classification, there is a method that utilizes the GP algorithm to construct classifiers for binary classification tasks. This method takes images as direct inputs to the GP program and performs operations such as feature extraction and feature construction simultaneously in this process. Ultimately, the GP program outputs a high-level feature for making classification decisions. Table 4 displays the GP-based binary image classification methods. These methods excel in binary image classification, but their performance in multiclass image classification has not been extensively studied yet. Another GP algorithm is applied to multiclass classification tasks which processes original images to either predict class labels directly or extract features at the root node. These features are then fed into a predefined classifier for classification. These high-level features are either applied to a fixed classification algorithm, or the root node directly outputs prediction labels for multiclass image classification. Table 5 displays the GP-based multiclass image classification methods. Furthermore, some studies focus on improving genetic operations with the goal of obtaining GP individuals with excellent classification performance during the iterative evolution process. Table 6 displays the methods of improvement on GP genetic operators. \begin{table}
\begin{tabular}{p{227.6 pt}} \[PERSON] et al. [27] designed a three-layer structure (classification, aggregation, and filtering layers) where the order of the layers is fixed, but the number of functions within each layer is not limited, allowing for varying depths in each layer. Experiments have shown that this structure has comparable performance to GP-based classifiers with domain knowledge. \\ \[PERSON] et al. [28] proposed an image classification method that combines useful operators of CNNs, such as convolution and pooling, with GP. This approach fully utilizes the flexibility of GP, enabling the learning of convolution filter coefficients and the extraction of features from useful regions of images to construct classifiers. \\ \hline [PERSON] et al. [29] proposed an automatic region detection method aimed at identifying and extracting important regions in images. This method not only significantly reduces the dimensionality of image data, thereby saving computational costs, but also automatically extracts and constructs effective features for efficient image classification. \\ \hline \end{tabular}
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Table 4: GP-based binary image classification methods. \begin{table}
\begin{tabular}{p{227.6 pt}} \hline [PERSON] et al. [24] developed CTransCNN, a hybrid multilabel medical image classification model combining CNN and Transformer, enhanced by MMAEF, MBR, and IIM modules, showing excellent performance and generalization in tests. \\ \hline [PERSON] et al. [25] developed DM-CNN, a new CNN model for medical image diagnosis, improving feature extraction and addressing uncertainty quantification, showing superior performance in various medical tests. \\ \hline [PERSON] et al. [26] proposed CEGAT, a fusion network for hyperspectral image classification, integrating CNN with an enhanced graph attention network, using spectral discrimination and attention modules, and a key sample selection strategy, achieving superior performance with limited labels. \\ \hline \end{tabular}
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Table 3: CNN-based image classification methods. GP-based image classification methods have achieved notable progress.