Some Researches On Semantic-based3D Model Retrieval Techniques

With rapid increases in3D models and continuous expansion, the semantic-based3D model retrieval as one of intelligent search methods attracts more and more attentions. Semantic-based retrieval is a model-searching way by using semantic knowledge based on knowledge extraction and semantic relatedness measure. Semantic-based retrieval plays a key role in3D model retrieval and reuse. As for the key problems of the semantic knowledge database creation, knowledge extraction, semantic metrics, based on the establishment of three-dimensional model, the semantic retrieval methods are studied as follows:1. Centering on the problem of how to extract geometric information feature of3D model, a feature extracting method is proposed based on the statistical characteristic--relative Angle histogram. The method satisfies invariance of the rotation, translation, scaling and has strong robustness. Comparing with other classical statistical feature extraction methods, this method has high retrieval recall and precision rates, and the average accuracy rate is over85%, which greatly improves the efficiency of three-dimensional model retrieval. The experimental results show that the proposed method is effective and feasible for semantic retrieval.2. Aiming at the problem of automatic classification of the three dimensionality (3D) models, according to the fact that the actual obtained data is always local linear in a low-dimensional manifold and each sample point can be represented with its neighbors, based on the UDP and LPP algorithms, a SSOLPP method is proposed and is applied to the3D-model-automatic-classification. The method makes use of the manifold structure of the large and high dimensionality data. The original data are projected to a low-dimensionality subspace by using the proposed method. In the low-dimensionality subspace, the within-class data are near to each other and the between-class data are far from each other. The experimental results on a real database show the effectiveness and feasibleness of the proposed method. 3. Aiming at the problem of supervised automatic classification, a kind of automatic classification method is proposed by using two-dimensional Hidden Markov Models. In the method, two-dimensional Hidden Markov Models are constructed by prior knowledge based on the machine learning theory. The experimental performance provide evidences that the proposed method can effectively improve the classification efficiency and accuracy of3D models repository.4. Aiming at the problem of build semantic web, the3D model ontology is defined and a layered semantic web search strategy is described. The ontology semantic web is constructed by using the hierarchical structure of knowledge database and based on WordNet extensions. The experimental results show that the method is more in line with the organizational structure of a3D model database and improves the recall ratio and precision of semantic retrieval and retrieval speed.5. Facing with the problem of semantic relatedness measurement for3D model ontology, a quick semantic-measurement method is proposed based on the features of the3D model. The method makes use of the human cognitive approach and the depth and breadth function to determine the relevance of the model. The experimental results show the propoed method has higher correlation with3D models and thus more consistent with general human perception. Based on the above analysis, a three-dimensional model of the semantic retrieval system and semantic search platform are designed and implemented. The platform has good scalability, which carry out the basis for the study of semantic retrieval.