Study On Feature Combination And Semantic-based Of 3D Models Retrieval

3D model is a natural and direct way to illustrate the real-world objects. With the proliferation of 3D models, it becomes an emergency task to obtain the desired models from the existing. This topic can facilitate the modeling process, and has great value in manufacturing, military, virtual reality, simulation etc... Therefore, 3D model retrieval emerges as an important field in multimedia retrieval, which aims at retrieving the desired models correctly, quickly and conveniently.The context-based 3D model retrieval is research focus at present due to the drawbacks of text-based retrieval. Nowadays, the context-based retrieval technique has many improvements in the theory and the applications. Researches on the context-based retrieval cover the topics including: (1) the feature extraction and similarity computation, the normalization of 3D models, etc.; (2) the classification and organization of 3D model database; (3) retrieval method and retrieval interface; (4) the construction of context-based 3D model retrieval system.Since the feature of 3D model determines the performance of a 3D model retrieval system, it is widely accepted that feature extraction of 3D model is the key problem of context-based method. Among the proposed feature extraction methods, most are shape-based methods.However, several different research groups prove that no feature-extraction method is perfect. Therefore, the researchers start to combine different 3D model features, while pursuing the perfect feature extraction method. The proposed methods combine different features by determining their weight. But all of these combination methods have their own shortcomings. Moreover, the fundamental topics related to the feature combination are still unnoticed, such as"the more the feature combined, the better"or otherwise. Since model's shape feature only reflects the physics information of model and can not represent its semantics, the shape-based method doesn't perform quite well due to the influence of Semantic Gap. Using the studies in the image or video retrieval for reference, semantic-based way can greatly improve the retrieval performance. However, this is still a novel topic in 3D model retrieval. Some researches adopt the relevance feedback to improve the performance, but do not solve the fundamental problems, like the semantic representation method of model.To solve these problems, the thesis conducts researches in the context-based 3D model retrieval and the semantic-based 3D model retrieval. For the context-based 3D model retrieval, the thesis concentrates on the combination of the shape feature in 3D model retrieval. The contributions of the thesis are stated as follows:(1) Analyzes 7 kinds of shape feature extraction methods. The experiments conducting on the classic 3D model database Princeton Shape Benchmark show that each kind of feature has its limitation which can not be breached through improving the particularity degree of the feature; and no method is best for all kinds of 3D models. These conclusions show the importance of feature combination.(2) Analyzes the influence of the feature weight, the feature type and the classification of 3D model on the performance of feature combination. Then, the thesis proposes the method to automatically decide the weight of different shape features and combines these features selectively; states the feature combination method based on iterative clustering in case no classification information is available. The experiments conduct on PSB show that the combined feature obtains the R-Precision 10.3%-16.5% higher than that of the best single feature. For the semantic-based 3D model retrieval, the thesis explores the way for representing semantic of 3D model.It is based on the precise semantic of each model and constructs the semantic tree of all models of PSB. The retrieval based on semantic tree returns related results for the 94% keywords of ZOO dataset, while the traditional method just return results for 2% keywords. And the interaction between semantic-based retrieval and context-based retrieval are also discussed in the thesis.On the whole, the thesis pursues the researches of several important topics in 3D model retrieval, and particularly concentrates on the application of clustering technique in content-based retrieval and the semantic retrieval using semantic tree.We have design the semantic-based model databases with the MySQL that is the open source software. We solve some problems that are access the databases, changing the characters and storage the big object. At the end, we test the retrieval system with the model data from Princeton benchmark databases. We have obtained the accuracy and effect with Weka that the other open source software. Weka is the best software in data mining field. And we draw the curve of Precision-Recall. We can make conclusion that the cluster technique can heighten t the retrieval efficiency and relevance feedback technology can higher the retrieval effect.In future works, the proposed methods in this thesis will be integrated in the efficiency and applicable 3D model retrieval system, in addition to perform in-depth researches based on current works.