...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information visualization >Visualization and recommendation of large image collections toward effective sensemaking
【24h】

Visualization and recommendation of large image collections toward effective sensemaking

机译：可视化和推荐大图像集合，以实现有效的感官

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

In our daily lives, images are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image collections and their associated text information. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visual navigation and comprehension of iGraph with tens of thousands of nodes and hundreds of millions of edges, we present a progressive solution that offers collection overview, node comparison, and visual recommendation. Our solution not only allows users to explore the entire collection with representative images and keywords but also supports detailed comparison for understanding and intuitive guidance for navigation. The visual exploration of iGraph is further enhanced with the implementation of bubble sets to highlight group memberships of nodes, suggestion of abnormal keywords or time periods based on text outlier detection, and comparison of four different recommendation solutions. For performance speedup, multiple graphics processing units and central processing units are utilized for processing and visualization in parallel. We experiment with two image collections and leverage a cluster driving a display wall of nearly 50million pixels. We show the effectiveness of our approach by demonstrating experimental results and conducting a user study.
机译：在我们的日常生活中，图像是我们需要处理的最常见的数据之一。我们介绍iGraph，这是一种基于图的方法，用于对大型图像集及其关联的文本信息进行可视化分析。给定这样一个集合，我们将计算图像之间的相似度，文本之间的距离以及图像和文本之间的连接，以构建iGraph，该复合图表示形式对这些图像和文本之间的基本关系进行编码。为了使iGraph具有数以万计的节点和数亿个边缘，可以实现有效的视觉导航和理解，我们提供了一种渐进式解决方案，提供了集合概述，节点比较和视觉推荐。我们的解决方案不仅允许用户使用具有代表性的图像和关键字来浏览整个集合，而且还支持进行详细的比较以了解和直观地导航。 iGraph的可视化探索通过气泡集突出显示节点的组成员身份，基于文本异常检测的异常关键字或时间段建议以及四种不同推荐解决方案的比较而得到进一步增强。为了提高性能，多个图形处理单元和中央处理单元被用于并行处理和可视化。我们尝试了两个图像集合，并利用一个群集驱动了将近5000万像素的显示墙。我们通过演示实验结果并进行用户研究来证明我们方法的有效性。

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