Content-Based Retrieval of Motion Capture Data Using Short-Term Feature Extraction

Jianfeng XU; Haruhisa KATO; Akio YONEYAMA

doi:10.1587/transinf.E92.D.1657

Content-Based Retrieval of Motion Capture Data Using Short-Term Feature Extraction

Jianfeng XU, Haruhisa KATO, Akio YONEYAMA

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This paper presents a content-based retrieval algorithm for motion capture data, which is required to re-use a large-scale database that has many variations in the same category of motions. The most challenging problem is that logically similar motions may not be numerically similar due to the motion variations in a category. Our algorithm can effectively retrieve logically similar motions to a query, where a distance metric between our novel short-term features is defined properly as a fundamental component in our system. We extract the features based on short-term analysis of joint velocities after dividing an entire motion capture sequence into many small overlapped clips. In each clip, we select not only the magnitude but also the dynamic pattern of the joint velocities as our features, which can discard the motion variations while keeping the significant motion information in a category. Simultaneously, the amount of data is reduced, alleviating the computational cost. Using the extracted features, we define a novel distance metric between two motion clips. By dynamic time warping, a motion dissimilarity measure is calculated between two motion capture sequences. Then, given a query, we rank all the motions in our dataset according to their motion dissimilarity measures. Our experiments, which are performed on a test dataset consisting of more than 190 motions, demonstrate that our algorithm greatly improves the performance compared to two conventional methods according to a popular evaluation measure P(N_R).

Publication: IEICE TRANSACTIONS on Information Vol.E92-D No.9 pp.1657-1667

Publication Date: 2009/09/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E92.D.1657

Type of Manuscript: PAPER

Category: Contents Technology and Web Information Systems

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Jianfeng XU, Haruhisa KATO, Akio YONEYAMA, "Content-Based Retrieval of Motion Capture Data Using Short-Term Feature Extraction" in IEICE TRANSACTIONS on Information, vol. E92-D, no. 9, pp. 1657-1667, September 2009, doi: 10.1587/transinf.E92.D.1657.
Abstract: This paper presents a content-based retrieval algorithm for motion capture data, which is required to re-use a large-scale database that has many variations in the same category of motions. The most challenging problem is that logically similar motions may not be numerically similar due to the motion variations in a category. Our algorithm can effectively retrieve logically similar motions to a query, where a distance metric between our novel short-term features is defined properly as a fundamental component in our system. We extract the features based on short-term analysis of joint velocities after dividing an entire motion capture sequence into many small overlapped clips. In each clip, we select not only the magnitude but also the dynamic pattern of the joint velocities as our features, which can discard the motion variations while keeping the significant motion information in a category. Simultaneously, the amount of data is reduced, alleviating the computational cost. Using the extracted features, we define a novel distance metric between two motion clips. By dynamic time warping, a motion dissimilarity measure is calculated between two motion capture sequences. Then, given a query, we rank all the motions in our dataset according to their motion dissimilarity measures. Our experiments, which are performed on a test dataset consisting of more than 190 motions, demonstrate that our algorithm greatly improves the performance compared to two conventional methods according to a popular evaluation measure P(N_R).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1657/_p

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@ARTICLE{e92-d_9_1657,
author={Jianfeng XU, Haruhisa KATO, Akio YONEYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Content-Based Retrieval of Motion Capture Data Using Short-Term Feature Extraction},
year={2009},
volume={E92-D},
number={9},
pages={1657-1667},
abstract={This paper presents a content-based retrieval algorithm for motion capture data, which is required to re-use a large-scale database that has many variations in the same category of motions. The most challenging problem is that logically similar motions may not be numerically similar due to the motion variations in a category. Our algorithm can effectively retrieve logically similar motions to a query, where a distance metric between our novel short-term features is defined properly as a fundamental component in our system. We extract the features based on short-term analysis of joint velocities after dividing an entire motion capture sequence into many small overlapped clips. In each clip, we select not only the magnitude but also the dynamic pattern of the joint velocities as our features, which can discard the motion variations while keeping the significant motion information in a category. Simultaneously, the amount of data is reduced, alleviating the computational cost. Using the extracted features, we define a novel distance metric between two motion clips. By dynamic time warping, a motion dissimilarity measure is calculated between two motion capture sequences. Then, given a query, we rank all the motions in our dataset according to their motion dissimilarity measures. Our experiments, which are performed on a test dataset consisting of more than 190 motions, demonstrate that our algorithm greatly improves the performance compared to two conventional methods according to a popular evaluation measure P(N_R).},
keywords={},
doi={10.1587/transinf.E92.D.1657},
ISSN={1745-1361},
month={September},}

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TY - JOUR
TI - Content-Based Retrieval of Motion Capture Data Using Short-Term Feature Extraction
T2 - IEICE TRANSACTIONS on Information
SP - 1657
EP - 1667
AU - Jianfeng XU
AU - Haruhisa KATO
AU - Akio YONEYAMA
PY - 2009
DO - 10.1587/transinf.E92.D.1657
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2009
AB - This paper presents a content-based retrieval algorithm for motion capture data, which is required to re-use a large-scale database that has many variations in the same category of motions. The most challenging problem is that logically similar motions may not be numerically similar due to the motion variations in a category. Our algorithm can effectively retrieve logically similar motions to a query, where a distance metric between our novel short-term features is defined properly as a fundamental component in our system. We extract the features based on short-term analysis of joint velocities after dividing an entire motion capture sequence into many small overlapped clips. In each clip, we select not only the magnitude but also the dynamic pattern of the joint velocities as our features, which can discard the motion variations while keeping the significant motion information in a category. Simultaneously, the amount of data is reduced, alleviating the computational cost. Using the extracted features, we define a novel distance metric between two motion clips. By dynamic time warping, a motion dissimilarity measure is calculated between two motion capture sequences. Then, given a query, we rank all the motions in our dataset according to their motion dissimilarity measures. Our experiments, which are performed on a test dataset consisting of more than 190 motions, demonstrate that our algorithm greatly improves the performance compared to two conventional methods according to a popular evaluation measure P(N_R).
ER -