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A news video mining method based on statistical analysis and visualization

3rd International conference on Image and Video Retrieval, CIVR 2004

作者： Xie, Yu-Xiang Luan, Xi-Dao Lao, Song-Yang Wu, Ling-Da Xiao, Peng Han, Zhi-Guang Centre for multimedia Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)3540225390

In this paper, we propose a novel news video mining method based on statistical analysis and visualization. We divide the process of news video mining into three steps: preprocess, news video data mining, and pattern visualization. In the first step, we concentrate on content-based segmentation, clustering and events detection to acquire the metadata. In the second step, we perform news video data mining by some statistical methods. Considering news videos’ features, in the analysis process we mainly concentrate on two factors: time and space. And in the third step, we try to visualize the mined patterns. We design two visualization methods: time-tendency graph and time-space distribution graph. Time-tendency graph is to reflect the tendencies of events, while time-space distribution graph is to reflect the relationships of time and space among various events. In this paper, we integrate news video analysis techniques with data mining techniques of statistical analysis and visualization to discover some implicit important information from large amount of news videos. Our experiments prove that this method is helpful for decision-making to some extent. © Springer-Verlag Berlin Heidelberg 2004.

关键词： visualization

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Color rapid prototyping for diffusion-tensor MRI visualization

Color rapid prototyping for diffusion-tensor MRI visualizati...

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Medical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International conference, Proceedings

作者： Acevedo, Daniel Zhang, Song Laidlaw, David H. Bull, Christopher W. Brown University Providence RI 02912 United States

ISBN: (纸本)3540229779

The use of color rapid prototyping (RP) plaster models as visualization tools to support scientific research in diffusion-tensor (DT) MRI analysis was analyzed. The surgeons and neurologist were given virtual-reality (VR) applications to visualize different aspects of their brain data. The physical representation of the virtual models allowed them to review the data with a very robust, natural and fast haptic interface. Results show that using information in the DT-MRI data, models are created with better structural stability.

关键词： Magnetic resonance imaging

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Exploratory analysis and visualization of speech and music by locally linear embedding

Exploratory analysis and visualization of speech and music b...

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Proceedings - IEEE International conference on Acoustics, Speech, and Signal Processing

作者： Jain, Viren Saul, Lawrence K. Department of Computer Science University of Pennsylvania Philadelphia PA 19104

Many problems in voice recognition and audio processing involve feature extraction from raw waveforms. The goal of feature extraction is to reduce the dimensionality of the audio signal while preserving the informative signatures that, for example, distinguish different phonemes in speech or identify particular instruments in music. If the acoustic variability of a data set is described by a small number of continuous features, then we can imagine the data as lying on a low dimensional manifold in the high dimensional space of all possible waveforms. Locally linear embedding (LLE) is an unsupervised learning algorithm for feature extraction in this setting. In this paper, we present results from the exploratory analysis and visualization of speech and music by LLE.

关键词： Speech recognition

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H-MDS: a new approach for interactive visualization with multidimensional scaling in the hyperbolic space

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INFORMATION SYSTEMS 2004年第4期29卷 273-292页

作者： Walter, JA Univ Bielefeld Dept Comp Sci Neuroinformat Grp D-33615 Bielefeld Germany

We introduce a novel projection-based visualization method for high-dimensional data sets by combining concepts from MDS and the geometry of the hyperbolic spaces. This approach hyperbolic multi-dimensional scaling (H-MDS) is a synthesis of two important concepts for explorative data analysis and visualization: (i) multi-dimensional scaling uses proximity or pair distance data to generate a low-dimensional, spatial presentation of the data;(ii) previous work on the "hyperbolic tree browser" demonstrated the extraordinary advantages for an interactive display of graph-like data in the two-dimensional hyperbolic space (H-2). In the new approach, H-MDS maps proximity data directly into the H-2 This removes the restriction to "quasi-hierarchical", graph-based data-a major limitation of (ii). Since a suitable distance function can convert all kinds of data to proximity (or distance-based) data, this type of data can be considered the most general. We review important properties of the hyperbolic space and, in particular, the circular Poincare model of the H-2. It enables effective human-computer interaction: by mouse dragging the "focus", the user can navigate in the data without loosing the context. In H-2 the "fish-eye" behavior originates not simply by a non-linear view transformation but rather by extraordinary, non-Euclidean properties of the H-2. Especially, the exponential growth of length and area of the underlying space makes the H-2 a prime target for mapping hierarchical and (now also) high-dimensional data. Several high-dimensional mapping examples including synthetic and real-world data are presented. Since high-dimensional data produce "ring"-shaped displays, we present methods to enhance the display by modulating the dissimilarity contrast. This is demonstrated for an application for unstructured text: i.e., by using multiple film critiques from news:*** and ***, each movie is placed within the H-2-creating a "space of movies" f

关键词： visualizing high-dimensional data hyperbolic multi-dimensional scaling H-MDS focus plus context semantic browsing text mining

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Web-GIS based SARS epidemic situation visualization

Web-GIS based SARS epidemic situation visualization

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4th International conference on Virtual Reality and Its Applications in Industry

作者： Lu, XL Zhejiang Univ Finance & Econ Sch Informat Technol Hangzhou 310012 Peoples R China

ISBN: (纸本)0819453676

In order to research, perform statistical analysis and broadcast the information of SARS epidemic situation;recording to the relevant spatial position, this paper proposed a unified global visualization information platform for SARS, epidemic situation based on Web-GIS and scientific virtualization technology. To setup the unified global visual information platform, the architecture of Web-GIS based interoperable information system is adopted to enable public report SARS virus information to health cure center visually by using the web visualization technology. A GIS java applet is used to visualize the relationship between spatial graphical data and virus distribution, and other web based graphics figures such as curves, bars, maps and multi-dimensional figures are used to visualize the relationship between SARS virus tendency with time, patient number or locations. The platform is designed to display the SARS information in real time, simulate visually for real epidemic situation and offer an analyzing tools for health department and the policy-making government department to support the decision-making for preventing against the SARS epidemic virus. It could be used to analyze the virus condition through visualized graphics interface, isolate the areas of virus source, and control the virus condition within shortest time. It could be applied to the visualization field of SARS preventing systems for SARS information broadcasting, data management, statistical analysis, and decision supporting.

关键词： SARS epidemic visualization Web-GIS SARS prevention CSCW

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Probabilistic principal surfaces for yeast gene microarray data mining

Probabilistic principal surfaces for yeast gene microarray d...

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4th IEEE International conference on data Mining

作者： Staiano, A De Vinco, L Ciaramella, A Raiconi, G Tagliaferri, R Amato, R Longo, G Donalek, C Miele, G Di Bernardo, D Univ Salerno Dipartimento Matemat & Informat I-84084 Fisciano Sa Italy

ISBN: (纸本)0769521428

The recent technological advances are producing huge data sets in almost all fields of scientific research, from astronomy to genetics. Although each research field often requires ad-hoc, fine tuned, procedures to properly exploit all the available information inherently present in the data, there is an urgent need for a new generation of general computational theories and tools capable to boost most human activities of data analysis. Here we propose Probabilistic Principal Surfaces (PPS) as an effective high-D data visualization and clustering, tool for data mining applications, emphasizing its flexibility and generality of use in data-rich field. In order to better illustrate the potentialities of the method, we also provide a real world case-study by discussing the use of PPS for the analysis of yeast gene expression levels from microarray chips.

关键词： data mining

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Discovering spatial patterns accurately with effective noise removal

Discovering spatial patterns accurately with effective noise...

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9th Workshop on Research Issues in data Mining and Knowledge Discovery, DMKD 2004, In Conjunction with ACM SIGMOD International conference on Management of data, SIGMOD-04

作者： Qian, Yu Zhang, Kang Department of Computer Science University of Texas at Dallas Richardson TX 75083-0688 United States

ISBN: (纸本)158113908X

Cluster analysis is a common approach to pattern discovery in spatial databases. While many clustering techniques have been developed, it is still challenging to discover implicit patterns accurately when the data set contains two kinds of noise or outliers: 1) domain-specific noise;2) noise similar to true data on size, shape, or density. This paper presents a two-step strategy to solve the problem effectively: firstly, groups of data points are separated into different layers according to their sizes and densities;then a layered visualization is provided to the user to separate noise and true data intuitively. Such a strategy not only produces user-desired results but also separates noise and true data accurately. After noise removal, a hierarchical clustering is performed on remaining data to discover natural clusters. The experimental studies on both benchmark data sets and real images show very encouraging results. Copyright 2004 ACM.

关键词： Cluster analysis

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Biomedical signal visualization and analysis system devlopment utilizing open source software and a computer cluster

Biomedical signal visualization and analysis system devlopme...

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Proceedings of the IEEE 30th Annual Northeast Bioengineering conference

作者： Tillett, Jason C. Phillips, Daniel B. Rochester Institute of Technology Rochester NY 14623 United States

The complex biomedical signals using Open Source software and Computer Cluster (CC) was described. The system was based on Open Source software at both system and application level to facilitate flexible development of complex computing paradigm. The system consisted of workstation or visualization terminal (VT) and CC. The analysis involved simple procedure which determined 200 point Fast Fourier Transform (FFT). The results show that the analysis required system level communication, generic data acquisition support and clinician interface issues.

关键词： Biomedical engineering

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Cross-weighted fisher discriminant analysis for visualization of DNA microarray data

Cross-weighted fisher discriminant analysis for visualizatio...

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Proceedings - IEEE International conference on Acoustics, Speech, and Signal Processing

作者： Zhang, Xinying Myers, Chad L. Kung, S.Y. Princeton University

Fisher's Discriminant analysis has recently shown promise in dimensionality reduction of high dimensional DNA data. However, the one-dimensional projection provided by this method is an optimal Bayesian classifier only when the intraclass data patterns are purely Gaussian distributed. Unfortunately, it has been well recognized that most DNA expression data are much more realistically represented by a Gaussian mixture model (GMM), which allows for multiple cluster centroids per class. When a data set from such a GMM is projected onto a one-dimensional subspace, its inherent multi-modal nature may be partially or completely obscured. Consequently, traditional Fisher DA is quite inadequate when higher dimensional visualization (e.g. 2-D or 3-D) is necessary. The proposed technique addresses this problem and makes use of combined supervised and unsupervised learning techniques for several DNA microarray signal processing functions, including intraclass cluster discovery, optimal projection, and identification/selection of responsible gene groups. In particular, a cross-weighted Fisher Discriminant analysis is proposed and its abilities to reduce dimensionality and to visualize data sets are evaluated.

关键词： DNA

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A visual interface for a multimodal interactivity annotation tool: Design issues and implementation solutions 04

A visual interface for a multimodal interactivity annotation...

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Working conference on Advanced Visual Interfaces, AVI 2004

作者： Kolodnytsky, Mykola Bernsen, Niels Ole Dybkjær, Laila NISLab. University of Southern Denmark Campusvej 55 5230 Odense Denmark

ISBN: (纸本)1581138679

This paper discusses the user interface design for the NITE WorkBench for Windows (NWB) which enables annotation and analysis of full natural interactive communicative behaviour between humans and between humans and systems. The system enables users to perceive voice and video data and control its presentation when performing multi-level, cross-level and cross-modality annotation, information visualisation for data coding and analysis, information retrieval, and data exploitation. © 2004 ACM.

关键词： data visualization

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