Multi-classification of pizza using computer vision and support vector machine

The classification of pizza base, sauce spread and topping is highly sensitive to human error for its subjective and inconsistent nature. Image processing techniques combined with machine learning provide an objective and consistent way to accomplish this task. By using a combination of several binary classifiers, support vector machine (SVM) is a state-of-the-art learning algorithm for multi-classification of pizza base, sauce spread, and topping. With the selected features as input, the one-versus-one and directed acyclic graph (DAG) methods achieved 89.17% and 88.33% multi-classification accuracy respectively for pizza base, both 87.5% for pizza sauce spread, and 80.83% and 80.00%, respectively for pizza topping. The results showed that the computer vision systems developed had a great potential to assist in the automatic multi-classification of pizza base, sauce spread, and topping.