Hi,
Testing and training images using a Random Forest classifier involves preparing your dataset, extracting features from images, training the model, and then evaluating its performance. You can make use of the “TreeBagger” function to train your random forest model. The step-wise method would be:
Step 1: Prepare the dataset
Step 2: Load the images and preprocess them, which might include resizing, normalization, or other transformations.
Step 3: Feature extraction, which is crucial for training a Random Forest. You can use features like HOG, SIFT, or simple pixel values.
Step 4: Train the random forest model. Refer to the example use of the function below for a better understanding:
% Train a Random Forest classifier
numTrees = 100; % Example number of trees
rfModel = TreeBagger(numTrees, features, labels, ...
'Method', 'classification');
Step 5: Evaluate the classifier.
For more information on the “TreeBagger” function refer to the below documentation:
