• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information visualization >Visual Data Mining Of Multimedia Data For Social And Behavioral Studies
【24h】

Visual Data Mining Of Multimedia Data For Social And Behavioral Studies

机译：用于社会和行为研究的多媒体数据的可视数据挖掘

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, and so on) has been collected in research laboratories in various scientific disciplines, particu-larly in cognitive and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling chal-lenge because most state-of-the-art data mining techniques can only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this challenge, we propose a hybrid approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) a smooth interface between visualization and data mining; (2) a flexible tool to explore and query temporal data derived from raw multimedia data; and (3) a seamless interface between raw multimedia data and derived data. We have developed various ways to visualize both temporal correlations and statistics of multiple derived variables as well as conditional and high-order statistics. Our visualization tool allows users to explore, compare and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data.
机译：随着计算技术的进步，各种科学学科的研究实验室，尤其是认知和行为研究中，已经收集了大量的高分辨率高质量多媒体数据（视频和音频等）。由于大多数最先进的数据挖掘技术只能从复杂的异构数据中搜索和提取预定义的模式或知识，因此如何自动有效地从丰富的多媒体数据中发现新知识构成了极大的挑战。鉴于这一挑战，我们提出了一种混合方法，使科学家可以首先使用数据挖掘，然后形成对当前结果进行可视化分析的闭环，然后通过可视化激发更多的数据挖掘工作，其结果可以依次进行可视化，并进行下一轮的视觉探索和分析。这样，从原始数据和当前分析水平中收集到的新见解和假设可有助于进一步分析。作为朝着这个目标迈出的第一步，我们实现了一个包含三个关键组件的可视化系统：（1）可视化和数据挖掘之间的平滑接口； （2）一种灵活的工具，用于探索和查询从原始多媒体数据中导出的时间数据； （3）原始多媒体数据和导出数据之间的无缝接口。我们已经开发了多种方法来可视化多个派生变量的时间相关性和统计以及条件和高阶统计。我们的可视化工具允许用户浏览，比较和分析多流派生变量，并同时切换以访问原始多媒体数据。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号