• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Computational Intelligence for Image Processing, 2009. CIIP '09 >Effective dimensionality reduction in multimedia applications
【24h】

Effective dimensionality reduction in multimedia applications

机译：有效降低多媒体应用程序的尺寸

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In multimedia information retrieval, multimedia data such as images and videos are represented as vectors in high-dimensional space. To search these vectors efficiently, a variety of indexing methods have been proposed. However, the performance of these indexing methods degrades dramatically with increasing dimensionality, which is known as the dimensionality curse. To resolve the dimensionality curse, dimensionality reduction methods have been proposed. They map feature vectors in high-dimensional space into vectors in low-dimensional space before the data are indexed. This paper proposes an improvement for the previously proposed dimensionality reduction. The previous method uses the norm and the approximated angle for every subvector. However, more storage space and a number of cosine computations are required because of multiple angle components. In this paper, we propose an alternative method employing a single angle component instead of respective angles for all the subvectors. Because only one angle for every subvector is considered, though the loss of information regarding the original data vector increases, which degrades the performance slightly, we can successfully reduce storage space as well as a number of cosine computations. Finally, we verify the superiority of the proposed approach via extensive experiments with synthetic and real-life data sets.
机译：在多媒体信息检索中，诸如图像和视频之类的多媒体数据被表示为高维空间中的向量。为了有效地搜索这些向量，已经提出了多种索引方法。但是，这些索引方法的性能随着维数的增加而急剧下降，这被称为维数诅咒。为了解决维数诅咒，提出了降维方法。他们在索引数据之前将高维空间中的特征向量映射到低维空间中的向量。本文对先前提出的降维提出了一种改进。先前的方法对每个子矢量使用范数和近似角度。但是，由于存在多个角度分量，因此需要更多的存储空间和余弦计算量。在本文中，我们提出了一种替代方法，该方法使用单个角度分量代替所有子矢量的各个角度。因为每个子矢量只考虑一个角度，所以尽管有关原始数据矢量的信息损失增加，从而使性能稍有下降，但我们可以成功减少存储空间以及进行余弦计算。最后，我们通过对合成和真实数据集进行大量实验，验证了该方法的优越性。

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