Automatic 3D Model Annotation by a Two-Dimensional Hidden Markov Model

In this paper, a new method of 3D model automatic annotation is proposed based on a twodimensional Hidden Markov Model (2-D HMM). Growing importance in the last years Hidden Markov Models are a widely used methodology for sequential data modeling. Recent years, HMMs are applied to research of automatic annotation, such as images and models annotation. The three basic problems with HMM-liked model are also solved in our model. Our modeling process has two steps, those are training and testing. In the proposed approach, each object is separated into several bins by a spiderweb model and a shape function D2 is computed for each bin. These feature vectors are then arranged in a sequential fashion to compose a sequence vector, which is used to train HMMs. In 2-D HMM, we assume that feature vectors are statistically dependent on an underlying state process which has transition probabilities conditioning the states of two neighboring bins. Thus the dependency of two dimensions is reflected simultaneously. To classify an object, the maximized posteriori probability is calculated by a given model and the observed sequence of an unknown object. Comparing with the general HMM, 2-D HMM gets more information from the neighboring bins. So the system of 2-D HMM performs well on images and model annotation. Analysis and experimental results show that the proposed approach performs better than existing ones in database.