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  1. Proceedings of the 2nd ACM international workshop on Multimedia databases (MMDB '04)
  2. Indexing of variable length multi-attribute motion data
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ABSTRACT Segmentation and Recognition of Multi-Attribute Motion Sequences ∗

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Indexing of variable length multi-attribute motion data

Content Provider ACM Digital Library
Author Prabhakaran, B. Pradhan, Gaurav Li, Chuanjun Zheng, S. Q.
Abstract Haptic data such as 3D motion capture data and sign language animation data are new forms of multimedia data. The motion data is multi-attribute, and indexing of multi-attribute data is important for quickly pruning the majority of irrelevant motions in order to have real-time animation applications. Indexing of multi-attribute data has been attempted for data of a few attributes by using R-tree or its variants after dimensionality reduction. In this paper, we exploit the singular value decomposition (SVD) properties of multi-attribute motion data matrices to obtain one representative vector for each of the motion data matrices of dozens or hundreds of attributes. Based on this representative vector, we propose a simple and efficient interval-tree based index structure for indexing motion data with large amount of attributes. At each tree level, only one component of the query vector needs to be checked during searching, comparing to all the components of the query vector that should get involved if an R-tree or its variants are used for indexing. Searching time is independent of the number of pattern motions indexed by the tree, making the index structure well scalable to large data repositories. Experiments show that up to 91∼93% irrelevant motions can be pruned for a query with no false dismissals, and the query searching time is less than 30 μ s with the existence of motion variations.
Starting Page 75
Ending Page 84
Page Count 10
File Format PDF
ISBN 1581139756
DOI 10.1145/1032604.1032617
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2004-11-13
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Dimensionality reduction Similarity Multi-attribute motion Indexing Singular value decomposition
Content Type Text
Resource Type Article
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