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3D mesh segmentation based on boundary strength

Ruan Jian Xue Bao/Journal of Software 2010年第SUPPL. 1期21卷 183-193页

作者： Sun, Xiao-Peng Ji, Yan-Jie Wei, Xiao-Peng Department of Computer and Information Technology Liaoning Normal University Dalian 116029 China School of Mechanical and Engineering Dalian University of Technology Dalian 116024 China Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian 116622 China

In this paper, we present a novel and fast algorithm for 3D meshes segmentation based on the geometric information of boundary. Firstly, according to the topological relationship of original mesh faces, we construct the dual graph, and set the weight of vertex and edge on the basis of geometric information of mesh faces. Using the k-way multilevel cutting method, we segment the dual graph quickly to obtain the pre-segmentation and initial boundary for each patch. Then we define the boundary strength function and the feature boundary of each patch, to construct a deformation contour on the patch's boundary. By minimizing the energy of the deformation contour, the initial boundary is driven to approach the feature boundary. Finally, the original mesh is segmented into several meaningful patches in accordance with the minimal rule. The experiment results suggest that our algorithm is efficient and effective, and is applicable to a variety of triangular mesh models with prominent shape features. © by Institute of Software, the Chinese Academy of Sciences.

关键词： Deformation

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A Laplacian eigenmaps based semantic similarity measure between words

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作者： Wu, Yuming Cao, Cungen Wang, Shi Wang, Dongsheng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No.6 Kexueyuan South Road Zhongguancun Beijing 100-190 China Graduate University of Chinese Academy of Sciences No. 19 Yu Quan Road Shi Jing Shan Distinct Beijing 100-049 China

ISBN: (纸本)3642163262

The measurement of semantic similarity between words is very important in many applicaitons. In this paper, we propose a method based on Laplacian eigenmaps to measure semantic similarity between words. First, we attach semantic features to each word. Second, a similarity matrix ,which semantic features are encoded into, is calculated in the original high-dimensional space. Finally, with the aid of Laplacian eigenmaps, we recalculate the similarities in the target low-dimensional space. The experiment on the Miller-Charles benchmark shows that the similarity measurement in the low-dimensional space achieves a correlation coefficient of 0.812, in contrast with the correlation coefficient of 0.683 calculated in the high-dimensional space, implying a significant improvement of 18.9%. © 2010 IFIP.

关键词： Semantics

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D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification

D-LDA: A Topic Modeling Approach without Constraint Generati...

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IEEE International Conference on Data Mining (ICDM)

作者： Fuzhen Zhuang Ping Luo Zhiyong Shen Qing He Yuhong Xiong Zhongzhi Shi Chinese Academy and Sciences China Hewlett Packard Laboratories China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China Innovation Works USA

We study what we call semi-defined classification, which deals with the categorization tasks where the taxonomy of the data is not well defined in advance. It is motivated by the real-world applications, where the unlabeled data may also come from some other unknown classes besides the known classes for the labeled data. Given the unlabeled data, our goal is to not only identify the instances belonging to the known classes, but also cluster the remaining data into other meaningful groups. It differs from traditional semi-supervised clustering in the sense that in semi-supervised clustering the supervision knowledge is too far from being representative of a target classification, while in semi-defined classification the labeled data may be enough to supervise the learning on the known classes. In this paper we propose the model of Double-latent-layered LDA (D-LDA for short) for this problem. Compared with LDA with only one latent variable y for word topics, D-LDA contains another latent variable z for (known and unknown) document classes. With this double latent layers consisting of y and z and the dependency between them, D-LDA directly injects the class labels into z to supervise the exploiting of word topics in y. Thus, the semi-supervised learning in D-LDA does not need the generation of pair wise constraints, which is required in most of the previous semi-supervised clustering approaches. We present the experimental results on ten different data sets for semi-defined classification. Our results are either comparable to (on one data sets), or significantly better (on the other nine data set) than the six compared methods, including the state-of-the-art semi-supervised clustering methods.

关键词： Data models Mathematical model Clustering algorithms Analytical models Companies Equations Web pages

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Combining the Missing Link: An Incremental Topic Model of Document Content and Hyperlink

Combining the Missing Link: An Incremental Topic Model of Do...

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International Asia-Pacific Web Conference (APWEB)

作者： Huifang Ma Zhixin Li Zhongzhi Shi Chinese Academy and Sciences Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN

The content and structure of linked information such as sets of web pages or research paper archives are dynamic and keep on changing. Even though different methods are proposed to exploit both the link structure and the content information, no existing approach can effectively deal with this evolution. We propose a novel joint model, called Link-IPLSI, to combine texts and links in a topic modeling framework incrementally. The model takes advantage of a novel link updating technique that can cope with dynamic changes of online document streams in a faster and scalable way. Furthermore, an adaptive asymmetric learning method is adopted to freely control the assignment of weights to terms and citations. Experimental results on two different sources of online information demonstrate the time saving strength of our method and indicate that our model leads to systematic improvements in the quality of classification and link prediction.

关键词： Laboratories intelligent structures information processing Computers Web pages Programmable control Adaptive control Learning systems Predictive models Data mining

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A HSC-based sample selection method for support vector machine

A HSC-based sample selection method for support vector machi...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qing He Ning Li Zhong-Zhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences China Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China Chinese Academy and Sciences China

Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high.

关键词： Support vector machines Training Classification algorithms Machine learning Accuracy Testing Machine learning algorithms

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Fast rate-distortion optimized transform for Intra coding

Fast rate-distortion optimized transform for Intra coding

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Picture Coding Symposium, PCS

作者： Xin Zhao Li Zhang Siwei Ma Wen Gao Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

In our previous work, the rate-distortion optimized transform (RDOT) is introduced for Intra coding, which is featured by the usage of multiple offline-trained transform matrix candidates. The proposed RDOT achieves remarkable coding gain for KTA Intra coding, while maintaining almost the same computational complexity at the decoder. However, at the encoder, the computational complexity is increased drastically by the expensive ratedistortion (R-D) optimized selection of transform matrix. To resolve this problem, in this paper, we propose a fast RDOT scheme using macroblock- and block-level R-D cost thresholding. With the proposed method, unnecessary mode trials and R-D evaluations of transform matrices can be efficiently skipped from the mode decision process. Extensive experimental results show that, with negligible coding performance degradation, about 88.9% of the total encoding time is saved by the proposed method.

关键词： Encoding Transforms Rate-distortion Acceleration Image coding Algorithm design and analysis Computational complexity

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Gray-scale super-resolution for face recognition from low Gray-scale resolution face images

Gray-scale super-resolution for face recognition from low Gr...

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IEEE International Conference on Image Processing

作者： Hu Han Shiguang Shan Xilin Chen Wen Gao Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

Today's camera sensors usually have a high gray-scale resolution, e.g. 256, however, due to the dramatic lighting variations, the gray-scales distributed to the face region might be far less than 256. Therefore, besides low spatial resolution, a practical face recognition system must also handle degraded face images of low gray-scale resolution (LGR). In the last decade, low spatial resolution problem has been studied prevalently, but LGR problem was rarely studied. Aiming at robust face recognition, this paper makes a first primary attempt to investigate explicitly the LGR problem and empirically reveals that LGR indeed degrades face recognition method significantly. Possible solutions to the problem are discussed and grouped into three categories: gray-scale resolution invariant features, gray-scale degradation modeling and Gray-scale Super-Resolution (GSR). Then, we propose a Coupled Subspace Analysis (CSA) based GSR method to recover the high gray-scale resolution image from a single input LGR image. Extensive experiments on FERET and CMU-PIE face databases show that the proposed method can not only dramatically increase the gray-scale resolution and visualization quality, but also impressively improve the accuracy of face recognition.

关键词： Image processing Conferences

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Predicting RNA secondary structure with Tabu Search

Predicting RNA secondary structure with Tabu Search

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IEEE International Conference on Cognitive Informatics

作者： Yongguo Liu Jianrui Hao Jun Peng Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Xiangtan China School of Computer Science and Engineering University of Electronic Science and Technology Chengdu China College of Electronic Information Engineering Chongqing University of Science and Technology Chongqing China

In this paper, a novel method for predicting RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS) is proposed. In RNATS, two search models, intensification search and diversification search, are designed to exploit the local regions around the current solution and explore the unvisited space, respectively. Simulation experiments are conducted for six RNA sequences to show that the proposed method is feasible and effective.

关键词： RNA Computer simulation Space exploration Neodymium Nickel Laboratories

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Maximizing intra-individual correlations for illumination-insensitive face recognition

Maximizing intra-individual correlations for illumination-in...

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IEEE International Conference on Image Processing

作者： Hu Han Shiguang Shan Xilin Chen Wen Gao Chinese Academy of Sciences Beijing China Institute of Computing Technology Key Laboratory of Intelligent Information Processing Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China

Illumination variation has been one of the most intractable problems in face recognition and many approaches have been proposed to handle illumination problem in the last decades of years. The key problem is how to get stable similarity measurements between two face images of the same individual but captured under dramatically different lighting conditions. We propose a framework to optimize the illumination normalization for a pair of gallery and probe face images by maximizing a correlation (MAC) between them. The illumination normalization in the proposed framework tends to maximize the intra-individual correlations instead of both the inter- and intra-individual correlations. Experiments on Extended YaleB and CMU-PIE face databases show the effectiveness of our proposed approach in face recognition across varying lighting conditions.

关键词： Image processing Conferences

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International Conference on Bio-Inspired computing: Theories and Applications, BICTA

作者： Zhujin Zhang Shuo Wang Xingyi Zhang Zheng Zhang School of Computer and Information Science University of South Australia Adelaide SA Australia Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

Randić et al. proposed a significant graphical representation for DNA sequences, which is very compact and avoids loss of information. In this paper, we build a fast algorithm for this graphical representation with time complexity O(n 2 ), and find another important advantage in the representation: no degeneracy. Moreover, we propose a new method to do similarity analysis of DNA sequences based on the representation. The approach adopts four elements of covariance matrix as a descriptor, and is illustrated on the first exon of beta-globin genes from 11 different species.

关键词： Rabbits Educational institutions Bovine Humans Sun DNA Mice

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