Classification of Image Features Using Random Forest Algorithm

Simulation implementation of random forest algorithm for image feature classification. This paper aims to explore methods for classifying image features and demonstrates how to implement simulations using the random forest algorithm. First, we introduce the background and current state of image feature classification. Second, we discuss in detail the principles and applications of the random forest algorithm, including comparisons with other classification algorithms. The implementation typically involves feature extraction using methods like HOG or SIFT, followed by constructing multiple decision trees through bootstrap aggregating. Next, we provide step-by-step simulation implementation procedures and present the results, along with analysis and discussion of experimental outcomes. The code implementation would include key functions for tree construction, feature sampling, and majority voting for final classification. Finally, we summarize the contributions and limitations of this study, and propose directions and suggestions for future research.