To arrive at such a multiclass classifier (that can differentiate between more than 40 different signs from the dataset), we need to perform the following steps:
- Preprocess the dataset: We need a way to load our dataset, extract the regions of interest, and split the data into appropriate training and test sets.
- Extract features: Chances are that raw pixel values are not the most informative representation of the data. We need a way to extract meaningful features from the data, such as features based on different color spaces and HOG.
- Train the classifier: We will train the multiclass classifier on the training data using a one-versus-all strategy.
- Score the classifier: We will evaluate the quality of the trained ensemble classifier by calculating different performance metrics, such as accuracy, precision, and recall.
We will discuss all these steps in detail...
