We focus on improving the effectiveness of content-based 3D shape retrieval. Motivated by retrieval performance of several ndividual 3D model feature vectors, we propose a novel method, called prior knowledge based automatic weighted combination, to improve the retrieval effectiveness. The method dynamically determines the weighting scheme for different feature vectors based on the prior knowledge. The experimental results show that the proposed method provides significant improvements on retrieval effectiveness of 3D shape search with several measures on a standard 3D database. Compared with two existing combination methods, the prior knowledge weighted combination technique has gained better retrieval effectiveness.

Since 3D models have been widely applied in many research areas, the techniques for content-based 3D model retrieval become necessary. In this paper, a novel visual based 3D shape descriptor called MATE is proposed. A modi-ed Principal component analysis (PCA) method for model normalization is presented at-rst. Sec-ondly, a new Adjacent angle distance Fourier (AADF) al-gorithm is proposed. Then the original two-viewed Dbucrer method is presented to extract characteristics of projected images. Finally, based on the modi-ed PCA method, the shape descriptor MATE is proposed by combining AADF, Tchebichef and two-viewed Dbu®er. Experimental results show that the descriptor MATE provides better retrieval performance than the best current descriptors.

基于内容的三维模型检索被广泛运用于许多研究领域。为了弥补特征提取算法描述模型特征的不足，提高三维模型的检索效果，该文提出了一种基于先验知识的三维模型特征向量动态选择算法。该算法利用查询模型计算各种特征向量的先验知识，然后动态地选择描述能力较强的特征向量计算模型之间的相似度距离。实验采用标准的模型库Princeton shape benchmark （PSB）和多种公认的评价方法，结果显示该算法提高了三维模型的检索效果，优于现有的2种流行的三维模型特征选择算法。

This paper proposes a general formalism for evaluating hybrid Bayesian networks. The formalism approximates a hybrid Bayesian network into the form, called fuzzy partial least-squares Bayesian network (FPBN). The form replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adding a directed link from that partner discrete variable to the continuous one. The partner discrete variable is acquired by the discretization of the original continuous variable with a fuzzification algorithm based on the structure adaptive-tuning neural network model. In addition, the dependence between the partner discrete variable and the original continuous variable is approximated by fuzzy sets, and the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution in which partial least-squares (PLS) is proposed as an alternative method for computing the vector of regression parameter. The experimental results are included to demonstrate the performances of the new approach.

As the need for personalized services sharply increases caused by the booming of Internet, Web-based data-mining is becoming a valuable sources of thoughts and theory to satisfy the personalized system function. The characters of personalized data-mining is reviewed and discussed in the beginning, and then an innovative algorithm (FP-Tree time-validity algorithm) of Web pageviews, based on personalization, is raised. More authentic information can be efficiently got by adding time-validity coefficient to FTT-Tree storage structure to implement increment mining.