3D Model Multiple Semantic Automatic Annotation for Small Scale Labeled Data Set

Abstract

Automatically assigning keywords to 3D models is of great interest as it allows one to retrieve, index, organize and understand large collections of 3D models. Most Methods require high sample size for training, so the data quality is in high demand. For small scale labeled data set, we propose a semi-supervised method to realize the 3D models multiple semantic annotation, which needs only a small amount of hand tagged information provided by users. The proposed technique utilizes low-level shape features and the keywords are assigned using a graphed-based label transfer mechanism to expand the training dataset. A weighted metric learning method is used to learn the distance measure from the extended dataset. Then multiple semantic annotation task can be completed on the learned distance measure. The proposed method outperforms the current state-of-the-art methods on the small scale labeled dataset and large unlabelled dataset. We believe that such measure will provide a strong platform to label 3D models when a small amount of labeled models were given.