TY - JOUR
T1 - Efficient Human Motion Retrieval via Temporal Adjacent Bag of Words and Discriminative Neighborhood Preserving Dictionary Learning
AU - Liu, Xin
AU - He, Gao Feng
AU - Peng, Shu Juan
AU - Cheung, Yiu Ming
AU - Tang, Yuan Yan
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2017/12
Y1 - 2017/12
N2 - Human motion retrieval from motion capture data forms the fundamental basis for computer animation. In this paper, the authors propose an efficient human motion retrieval approach via temporal adjacent bag of words (TA-BoW) and discriminative neighborhood preserving dictionary learning (DNP-DL). The retrieval process includes two phases: offline training and online retrieval. In the first phase, the original skeleton model is first simplified and then pairwise joint distances are computed to characterize each motion frame. Then, a novel motion descriptor, namely TA-BoW, is proposed to discriminatively code the motion appearances, through which the articulated complexity and spatiotemporal dimensionality can be greatly reduced. Subsequently, by considering the neighborhood relationships of intraclass structure and the advantage of Fisher criterion, a DNP-DL method is exploited through which each human action can be discriminatively and sparsely represented by a linear combination of such dictionary atoms. In the second phase, a hierarchical retrieval mechanism is used by incorporating the sparse classification and chi-square ranking, whereby the searching range is significantly reduced. The experimental results show that the proposed human motion retrieval approach performs better than the state-of-the-art competing approaches.
AB - Human motion retrieval from motion capture data forms the fundamental basis for computer animation. In this paper, the authors propose an efficient human motion retrieval approach via temporal adjacent bag of words (TA-BoW) and discriminative neighborhood preserving dictionary learning (DNP-DL). The retrieval process includes two phases: offline training and online retrieval. In the first phase, the original skeleton model is first simplified and then pairwise joint distances are computed to characterize each motion frame. Then, a novel motion descriptor, namely TA-BoW, is proposed to discriminatively code the motion appearances, through which the articulated complexity and spatiotemporal dimensionality can be greatly reduced. Subsequently, by considering the neighborhood relationships of intraclass structure and the advantage of Fisher criterion, a DNP-DL method is exploited through which each human action can be discriminatively and sparsely represented by a linear combination of such dictionary atoms. In the second phase, a hierarchical retrieval mechanism is used by incorporating the sparse classification and chi-square ranking, whereby the searching range is significantly reduced. The experimental results show that the proposed human motion retrieval approach performs better than the state-of-the-art competing approaches.
KW - Hierarchical retrieval mechanism
KW - human motion retrieval
KW - neighborhood preserving dictionary
KW - pairwise distance
KW - temporal adjacent bag of words (TA-BoW)
UR - http://www.scopus.com/inward/record.url?scp=85016420840&partnerID=8YFLogxK
U2 - 10.1109/THMS.2017.2675959
DO - 10.1109/THMS.2017.2675959
M3 - Journal article
AN - SCOPUS:85016420840
SN - 2168-2291
VL - 47
SP - 763
EP - 776
JO - IEEE Transactions on Human-Machine Systems
JF - IEEE Transactions on Human-Machine Systems
IS - 6
M1 - 7885610
ER -