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2008 Chinese Conference on Pattern Recognition最新文献

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Conditional Bayesian Network Mix Classifiers using on Performance -Appraising of Enterprise 条件贝叶斯网络混合分类器在企业绩效评价中的应用
2008 Chinese Conference on Pattern Recognition Pub Date : 2008-10-31 DOI: 10.1109/CCPR.2008.20
Shuangcheng Wang, Ying-Qi Li, Qing Liu
{"title":"Conditional Bayesian Network Mix Classifiers using on Performance -Appraising of Enterprise","authors":"Shuangcheng Wang, Ying-Qi Li, Qing Liu","doi":"10.1109/CCPR.2008.20","DOIUrl":"https://doi.org/10.1109/CCPR.2008.20","url":null,"abstract":"At present, some deficiencies exist in the methods using to appraising enterprise performance. And there are the problems of efficiency and reliability in learning conditional Bayesian network. In this paper, a conditional Bayesian network structure is established by using sorting nodes and local search & scoring. And a conditional Bayesian network is combined with a naive Bayes classifier to form a conditional Bayesian network mix classifier. The classifier is used in appraising enterprise performance to make evaluation more science.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116719939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Nonparametric Motion Feature for Key Frame Extraction in Sports Video 运动视频关键帧提取的非参数运动特征
2008 Chinese Conference on Pattern Recognition Pub Date : 2008-10-31 DOI: 10.1109/CCPR.2008.43
Li Li, Xiaoqin Zhang, Yangping Wang, Weiming Hu, Peng Fei Zhu
{"title":"Nonparametric Motion Feature for Key Frame Extraction in Sports Video","authors":"Li Li, Xiaoqin Zhang, Yangping Wang, Weiming Hu, Peng Fei Zhu","doi":"10.1109/CCPR.2008.43","DOIUrl":"https://doi.org/10.1109/CCPR.2008.43","url":null,"abstract":"Key frames extraction play an important role in video abstraction. Traditional key frame extraction methods only use color, texture, or shape features to represent a frame, while the motion feature is ignored or inappropriately modeled. Since the motion feature contains a lot of semantic information in video analysis, we propose a compact representation of the dominant motion information for each frame, based on a mean shift analysis procedure. Then, an EMD (Earth mover's distance) is employed as a similarity metric for the represented motion feature. Moreover, we propose a novel temporal k-means clustering algorithm for the key frame extraction, which naturally incorporates the sequential constraint into extracted key frames. Experimental results demonstrate the effectiveness of our approach.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126612655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
A Novel Algorithm of Target Pseudo-Color Fusion Based on Image Features 一种基于图像特征的目标伪颜色融合算法
2008 Chinese Conference on Pattern Recognition Pub Date : 2008-10-31 DOI: 10.1109/CCPR.2008.39
Qingwang Qin, Tingfa Xu, Man-jun Xiao, Guoqiang Ni
{"title":"A Novel Algorithm of Target Pseudo-Color Fusion Based on Image Features","authors":"Qingwang Qin, Tingfa Xu, Man-jun Xiao, Guoqiang Ni","doi":"10.1109/CCPR.2008.39","DOIUrl":"https://doi.org/10.1109/CCPR.2008.39","url":null,"abstract":"A novel target pseudo-color image fusion algorithm for visual and infrared images is proposed in this paper based on image features in wavelet domain. After wavelet decomposed from source images, edge features are extracted from each low frequency component. The fusion rules are defined by the edge information, using local modulus maximum rule for edge pixels and its sub-band neighboring pixels, and weighted mean coefficient rule for non-edge pixels. The interested targets are extracted respectively by threshold segmentation method for infrared image and moving targets detection method for motion image sequences. Fusion result is mapped into local pseudo-color image in HSV color space, in order to highlight infrared hot targets and moving targets. The simulation experiments show that the fusion image has good visual effect, and also has good performance on the objective evaluation and anti-noise ability.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129277212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Two-Dimensional Local Graph Embedding Analysis(2DLGEA) for Face Recognition 二维局部图嵌入分析(2DLGEA)用于人脸识别
2008 Chinese Conference on Pattern Recognition Pub Date : 2008-10-31 DOI: 10.1109/CCPR.2008.60
M. Wan, Zhihui Lai, Zhong Jin
{"title":"Two-Dimensional Local Graph Embedding Analysis(2DLGEA) for Face Recognition","authors":"M. Wan, Zhihui Lai, Zhong Jin","doi":"10.1109/CCPR.2008.60","DOIUrl":"https://doi.org/10.1109/CCPR.2008.60","url":null,"abstract":"This paper proposes a novel method, called two-dimensional local graph embedding analysis (2DLGEA), for image feature extraction, which can directly extracts the optimal projective vectors from 2D image matrices rather than image vectors based on the scatter difference criterion. In graph embedding, the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring within the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. The proposed method effectively avoids the singularity problem frequently occurred in the classical linear discriminant analysis due to the small sample size and overcomes the limitations of the traditional linear discriminant analysis algorithm due to data distribution assumptions and available projection directions. Experimental results on Yale, and ORL face databases show the effectiveness of the proposed method.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"289 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133314094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Semi-Supervised Clustering Algorithm for Multi-Density and Complex Shape Dataset 多密度复杂形状数据集的半监督聚类算法
2008 Chinese Conference on Pattern Recognition Pub Date : 2008-10-31 DOI: 10.1109/CCPR.2008.15
Yangqiang Yu, Tian-qiang Huang, Gong-de Guo, Kai Li
{"title":"Semi-Supervised Clustering Algorithm for Multi-Density and Complex Shape Dataset","authors":"Yangqiang Yu, Tian-qiang Huang, Gong-de Guo, Kai Li","doi":"10.1109/CCPR.2008.15","DOIUrl":"https://doi.org/10.1109/CCPR.2008.15","url":null,"abstract":"There are many complicated data in real world, clustering analysis should be able to find the clusters of different shapes and densities. The existing typical clustering algorithms do not perform well on multi-density data. A semi-supervised clustering algorithm for multi-density dataset SCMD is proposed. The pairwise constraints: must-link and cannot-link that reflect the distribution of multi-density dataset are used. Experimental results show the algorithm can identify the clusters of varying shapes, sizes, and densities, even in the presence of noise and outliers. It is more efficient than SNN and DBSCAN.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132165272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
ICA Based Minimum Discriminant Analysis and Its Application to Face Recognition 基于ICA的最小判别分析及其在人脸识别中的应用
2008 Chinese Conference on Pattern Recognition Pub Date : 2008-10-31 DOI: 10.1109/CCPR.2008.56
Jianguo Wang, Wankou Yang, Hui Yan, Jingyu Yang
{"title":"ICA Based Minimum Discriminant Analysis and Its Application to Face Recognition","authors":"Jianguo Wang, Wankou Yang, Hui Yan, Jingyu Yang","doi":"10.1109/CCPR.2008.56","DOIUrl":"https://doi.org/10.1109/CCPR.2008.56","url":null,"abstract":"Face recognition is a very active field for research in the field of pattern recognition. To improve the performance of feature extraction in face recognition, a novel feature extraction method named as minimal linear discriminant analysis based on independent component analysis (ICA) is proposed. Therefore, the singular problem of the within-class scatter matrix will be avoided, and linear discriminant vectors with most discriminant information can be obtained. Experimental results on Yale and ORL face databases demonstrate that the recognition rate of the proposed method is more effective than that of the classical methods.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130076747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Homogeneous Description for Heterogeneous Cross-Media Resources 异构跨媒体资源的同构描述
2008 Chinese Conference on Pattern Recognition Pub Date : 2008-10-31 DOI: 10.1109/CCPR.2008.82
Dong Xu, Ping Hu, Hua Li
{"title":"Homogeneous Description for Heterogeneous Cross-Media Resources","authors":"Dong Xu, Ping Hu, Hua Li","doi":"10.1109/CCPR.2008.82","DOIUrl":"https://doi.org/10.1109/CCPR.2008.82","url":null,"abstract":"Images, polygonal models and point clouds are three kinds of heterogeneous cross-media resources, but they can stand for the same object. We introduce two kinds of homogeneous descriptions for them. First, we find a relation between surface moment invariants and shape distribution since they both use invariant geometric primitives. This relation can provide a way to approximate surface moment invariants for point clouds. Hence, we can get the same shape description for both point clouds and polygonal models. Second, we project each 3D model in many directions and get large numbers of 2D images. Then, polygonal models and images are converted to the same data format for comparison. Homogeneous shape description makes it possible to carry out heterogeneous cross-media retrieval in pervasive environment.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117184654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Skeletonization of Branched Volume by Shape Decomposition 基于形状分解的分支体骨架化
2008 Chinese Conference on Pattern Recognition Pub Date : 2008-10-31 DOI: 10.1109/CCPR.2008.31
Bo Xiang, Xiaopeng Zhang, Wei Ma, H. Zha
{"title":"Skeletonization of Branched Volume by Shape Decomposition","authors":"Bo Xiang, Xiaopeng Zhang, Wei Ma, H. Zha","doi":"10.1109/CCPR.2008.31","DOIUrl":"https://doi.org/10.1109/CCPR.2008.31","url":null,"abstract":"We present an algorithm to automatically extract skeletons for branched volumes by shape decomposition. First, a region growing strategy is adopted based on a distance transformation to decompose a volume into several meaningful components with simple topological structures. Then, the skeleton of each component is individually extracted. Finally, the skeletons of all the components are integrated and a structural skeleton of the volume data is obtained, where the structural skeleton is topologically equivalent to the volume. The contributions of the algorithm are: the elimination of the influence of different branches and the accurate skeleton extraction with topological structure of the model due to exact decomposition. Experiments show that this algorithm is applicable to shapes with complex topology.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123315849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Background Modeling Method Based on Sequential Kernel Density Approximation 基于序列核密度近似的背景建模方法
2008 Chinese Conference on Pattern Recognition Pub Date : 2008-10-31 DOI: 10.1109/CCPR.2008.44
Huan Wang, Mingwu Ren, Jing-yu Yang
{"title":"Background Modeling Method Based on Sequential Kernel Density Approximation","authors":"Huan Wang, Mingwu Ren, Jing-yu Yang","doi":"10.1109/CCPR.2008.44","DOIUrl":"https://doi.org/10.1109/CCPR.2008.44","url":null,"abstract":"Background subtraction is a popular moving object detection technique, but its performance is dependent of the accuracy of background model. In this paper, the theory of sequential kernel density approximation is first introduced to background modeling. To this end, a novel background subtraction method for moving object detection is proposed. Various real video sequences have been used to test this method, and comparisons with other standard background subtraction methods also demonstrate that the sequential kernel density approximation is well-suited for background modeling, and the proposed method is effectiveness, it can be efficiently used in various real-time moving object detection systems.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132436292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
An Improved Algorithm for Subpixel Location of Circle Center 一种改进的亚像素圆中心定位算法
2008 Chinese Conference on Pattern Recognition Pub Date : 2008-10-31 DOI: 10.1109/CCPR.2008.24
Hu Zhang, F. Da, De-Kui Xing
{"title":"An Improved Algorithm for Subpixel Location of Circle Center","authors":"Hu Zhang, F. Da, De-Kui Xing","doi":"10.1109/CCPR.2008.24","DOIUrl":"https://doi.org/10.1109/CCPR.2008.24","url":null,"abstract":"To obtain the center of a circle in the digital image with high precision is always the key issue in the target recognition and location. Starting from the subpixel edge location, a improved algorithm for obtaining the subpixel edge, in this paper, is first brought forward based on the geometrical feature of the center and gray-distributed characteristic of the practical image. Then, the center location is realized by the least square method. Several experiments are used to compare this new algorithm and currently existing algorithms, and through the results, it can be concluded that the improved algorithm has much higher location precision and better robustness.","PeriodicalId":292956,"journal":{"name":"2008 Chinese Conference on Pattern Recognition","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124352396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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