Research On Feature Description And Relevance Feedback Algorithms For 3d Model Retrieval

In the field of content-based 3D model retrieval, feature descriptors and relevance feedback algorithms are significant technologies. Although feature descriptors are able to extract various low-level characteristics, a few problems in the procedure of feature extraction need to be improved. 3D model relevance feedback methods can acquire users'semantic knowledge and improve retrieval effectiveness, but the troubles of sample filtering and others universally exist in such approaches. In order to deal with the difficulties discussed above, this dissertation systematically investigates on the area and proposes a series of algorithms to solve the problems.The main work in this dissertation includes:1. A visual based 3D model feature descriptor MATE is presented. This algorithm firstly proposes a modified PCA method, and then introduces an adjacent anlge distance Fourier descriptor, presents an improved depth buffer descriptor in succession, and finally combines three different kinds of descriptors. Compared with several 3D model descriptors, MATE not only acquires better retrieval effectiveness with a few standard evaluation methods, but also solves the tradeoff trouble between retrieval effectiveness and feature dimension.2. A prior knowledge based feature vectors combination method for 3D model retrieval is proposed. It calculates prior konwlege of different feature vectors using query model, and then dynamically allocates weight for feature vectors to combine a feature vector. Experimental results show that this method is able to make use of various advantages of feature vectors, and its retrieval effectiveness is obviously better than two state-of-the-art algorithms.3. A powerful relevance feedback mechanism for content-based 3D model retrieval is introduced. This approach utilizes several feature vectors to describe different low-level feature information, and acquires users'retrieval requirement accurately. Experimental results illustrates that this method quickly narrows the gap between low-level feature information and high-level semantic knowledge, and significantly imporves 3D model retrieval effectiveness. Compared with several algorithms, this method obtains evident superiority with several standard evaluation approaches, and achieves preferable retrieval effectiveness with two rounds of relevance feedback.4. A long-term learning mechanism based SVM active learning relevance feedback algorithm is presented. This method returns the most informative models with active learning mechanism, preserves retrieval record and marking information of users, mines semantic konwlege hidden among different models by Laplacian Eigenmaps, and finally retrieves models with similarity measuring in semantic space. Compared with relevance feedback algorithms in 3D model area, this method not only accurately acquires users'semantic konwlege, but also significantly improves 3D model retrieval effectiveness. It achieves perfect retrieval result only with one or two rounds of relevance feedback.