Image Classification Program

This article discusses an image classification program consisting of four core functions. While the author provides limited implementation details, we can infer the program's purpose is to categorize images into distinct classes. Each function plays a critical role in the classification pipeline, likely including components for image preprocessing, feature extraction, model training, and classification inference. To enhance program performance while maintaining operational stability, developers can either increase the number of specialized functions or optimize existing code implementations. For instance, adding a dedicated "Data Preprocessing" function could handle input data cleaning and normalization using techniques like min-max scaling or z-score standardization, thereby improving classification accuracy. Additionally, optimizing the "Feature Extraction" function could involve implementing advanced algorithms like Histogram of Oriented Gradients (HOG) or deep learning-based feature encoders to better capture critical image characteristics. Overall, this image classification framework demonstrates substantial application potential across various domains, with opportunities for continuous optimization and expansion to meet diverse requirements through modular function enhancements and algorithm improvements.