• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Graph-based representations in pattern recognition >Classification of Graph Sequences Utilizing the Eigenvalues of the Distance Matrices and Hidden Markov Models
【24h】

Classification of Graph Sequences Utilizing the Eigenvalues of the Distance Matrices and Hidden Markov Models

机译：利用距离矩阵的特征值和隐马尔可夫模型对图序列进行分类

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 this paper, the classification of human activities based on sequences of camera images utilizing hidden Markov models is investigated. In the first step of the proposed data processing procedure, the locations of the person's body parts (hand, head, etc.) and objects (table, cup, etc.) which are relevant for the classification of the person's activity have to be estimated for each camera image. In the next processing step, the distances between all pairs of detected objects are computed and the eigenvalues of this Euclidean distance matrix are calculated. This set of eigenvalues built the input for a single camera image and serve as the inputs to Gaussian mixture models, which are utilized to estimate the emission probabilities of hidden Markov models. It could be demonstrated, that the eigenvalues are powerful features, which are invariant with respect to the labeling of the nodes (if they are utilized sorted by size) and can also deal with graphs, which differ in the number of their nodes.
机译：本文研究了利用隐马尔可夫模型基于摄像机图像序列的人类活动分类。在建议的数据处理过程的第一步中，必须估算与人体活动分类相关的人体部位（手，头等）和物体（桌子，杯子等）的位置对于每个相机图像。在下一处理步骤中，计算所有被检测物体对之间的距离，并计算该欧几里得距离矩阵的特征值。这组特征值建立了单个摄像机图像的输入，并用作高斯混合模型的输入，这些模型用于估计隐马尔可夫模型的发射概率。可以证明，特征值是强大的功能，对于节点的标签（如果按大小将其使用）不变，并且还可以处理节点数不同的图。

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