Random Forest for Image Processing

Random Forest is an ensemble learning algorithm based on decision trees that finds extensive applications in machine learning. In image processing, it can be effectively utilized for classification, segmentation, and detection tasks. Within MATLAB, the Random Forest algorithm can be implemented to process digital images through functions like TreeBagger or fitensemble, which allow customization of tree numbers and split criteria. The algorithm works by creating multiple decision trees during training and outputting the mode of classes (classification) or mean prediction (regression) from individual trees. This ensemble approach significantly improves processing accuracy and computational efficiency compared to single decision trees. Code implementation is relatively straightforward, requiring minimal configuration of hyperparameters such as the number of trees, maximum depth, and split criterion. Key MATLAB functions involve data preprocessing, model training with fitensemble, and prediction using predict functions. Consequently, Random Forest serves as a highly valuable tool in image processing applications, warranting broad adoption and further research in computer vision domains.