首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing;ICASSP 2009 >Dynamic updating and downdating matrix SVD and tensor HOSVD for adaptive indexing and retrieval of motion trajectories
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Dynamic updating and downdating matrix SVD and tensor HOSVD for adaptive indexing and retrieval of motion trajectories

机译:动态更新和降级矩阵SVD和张量HOSVD,用于运动轨迹的自适应索引和检索

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Motion information is regarded as one of the most important cues for developing semantics in video data. Yet it is extremely challenging to build indexing and browsing tools for video data, particularly when it involves interactive motions of multiple objects. The problem is further complicated when the video archives are dynamically updated, and/or queries contains partial information. An efficient solution would require that the feature space used to represent the data be dynamically updated or downdated to allow frequent additions/deletions and matching of queries. Assuming tensor HOSVD as the feature space, in this paper, we propose two novel algorithms, namely, dynamic tensor HOSVD updating algorithm (DTSV D
机译:运动信息被认为是在视频数据中发展语义的最重要线索之一。然而,为视频数据建立索引和浏览工具极具挑战性,特别是当它涉及多个对象的交互运动时。当动态地更新视频档案和/或查询包含部分信息时,问题变得更加复杂。一种有效的解决方案将要求动态地更新或缩减用于表示数据的特征空间,以允许频繁的添加/删除和查询匹配。假设张量HOSVD为特征空间,本文提出了两种新颖的算法,即动态张量HOSVD更新算法(DTSV D

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