LAUSR

The main objective of classifying parts of a 3D object is to group them into similar and meaningful categories. This purpose is a challenging task in pattern recognition, especially when the risk of assigning simultaneously the same label of a class to many homogenous categories is high. Hence, this paper aims to develop a robust algorithm for 3D objects-parts recognition based on a hybrid approach combining a boosting algorithm and a SVM classifier. This approach called Boosted-SVM consists of two steps, the first one is training the strong SVM classifier to be a weak base learner, and the second one is combing these trained classifiers into a strong one using the Adaboost algorithm. The training of the classification model is based on a significant and robust feature vector representing parts of the 3D object, the Shape Spectrum Descriptor (SSD) is used for this purpose. The performed experimental results have shown excellent recognition rate and very short training time of the built system.