• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Machine Vision and Applications >E-LAMP: integration of innovative ideas for multimedia event detection
【24h】

E-LAMP: integration of innovative ideas for multimedia event detection

机译：E-LAMP：整合创新思想进行多媒体事件检测

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

Detecting multimedia events in web videos is an emerging hot research area in the fields of multimedia and computer vision. In this paper, we introduce the core methods and technologies of the framework we developed recently for our Event Labeling through Analytic Media Processing (E-LAMP) system to deal with different aspects of the overall problem of event detection. More specifically, we have developed efficient methods for feature extraction so that we are able to handle large collections of video data with thousands of hours of videos. Second, we represent the extracted raw features in a spatial bag-of-words model with more effective tilings such that the spatial layout information of different features and different events can be better captured, thus the overall detection performance can be improved. Third, different from widely used early and late fusion schemes, a novel algorithm is developed to learn a more robust and discriminative intermediate feature representation from multiple features so that better event models can be built upon it. Finally, to tackle the additional challenge of event detection with only very few positive exemplars, we have developed a novel algorithm which is able to effectively adapt the knowledge learnt from auxiliary sources to assist the event detection. Both our empirical results and the official evaluation results on TRECVID MED'11 and MED'12 demonstrate the excellent performance of the integration of these ideas.
机译：在网络视频中检测多媒体事件是多媒体和计算机视觉领域的新兴研究热点。在本文中，我们将介绍我们最近通过分析媒体处理（E-LAMP）系统为事件标记开发的框架的核心方法和技术，以处理事件检测总体问题的各个方面。更具体地说，我们已经开发了有效的特征提取方法，从而能够处理数千小时视频的大量视频数据。其次，我们将提取的原始特征表示在具有更有效切片的空间词袋模型中，从而可以更好地捕获不同特征和不同事件的空间布局信息，从而可以提高整体检测性能。第三，不同于广泛使用的早期和晚期融合方案，开发了一种新颖的算法来从多个特征中学习更鲁棒和有区别的中间特征表示，以便可以在其上构建更好的事件模型。最后，为了仅用很少的正例来解决事件检测的其他挑战，我们开发了一种新颖的算法，该算法能够有效地适应从辅助资源中学到的知识，以辅助事件检测。我们在TRECVID MED'11和MED'12上的实证结果和官方评估结果都证明了这些思想的整合的出色表现。

著录项

  • 来源
    《Machine Vision and Applications》 |2014年第1期|5-15|共11页
  • 作者

    Wei Tong; Yi Yang; Lu Jiang; Shoou-I Yu; ZhenZhong Lan; Zhigang Ma; Waito Sze; Ehsan Younessian; Alexander G. Hauptmann;

  • 作者单位

    Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA;

    Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA;

    Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA;

    Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA;

    Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA;

    Department of Information Engineering and Computer Science, University of Trento, Trento, Italy;

    Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA;

    Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA;

    Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Multimedia event detection; Multimedia content analysis;

    机译：多媒体事件检测;多媒体内容分析;

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