Biview learning for human posture segmentation from 3D points cloud

Journal article


Qiao, Maoying, Cheng, Jun, Bian, Wei and Tao, Dacheng. (2014). Biview learning for human posture segmentation from 3D points cloud. PLoS ONE. 9(1), p. e85811. https://doi.org/10.1371/journal.pone.0085811
AuthorsQiao, Maoying, Cheng, Jun, Bian, Wei and Tao, Dacheng
Abstract

Posture segmentation plays an essential role in human motion analysis. The state-of-the-art method extracts sufficiently high-dimensional features from 3D depth images for each 3D point and learns an efficient body part classifier. However, high-dimensional features are memory-consuming and difficult to handle on large-scale training dataset. In this paper, we propose an efficient two-stage dimension reduction scheme, termed biview learning, to encode two independent views which are depth-difference features (DDF) and relative position features (RPF). Biview learning explores the complementary property of DDF and RPF, and uses two stages to learn a compact yet comprehensive low-dimensional feature space for posture segmentation. In the first stage, discriminative locality alignment (DLA) is applied to the high-dimensional DDF to learn a discriminative low-dimensional representation. In the second stage, canonical correlation analysis (CCA) is used to explore the complementary property of RPF and the dimensionality reduced DDF. Finally, we train a support vector machine (SVM) over the output of CCA. We carefully validate the effectiveness of DLA and CCA utilized in the two-stage scheme on our 3D human points cloud dataset. Experimental results show that the proposed biview learning scheme significantly outperforms the state-of-the-art method for human posture segmentation.

Year2014
JournalPLoS ONE
Journal citation9 (1), p. e85811
PublisherPublic Library of Science
ISSN1932-6203
Digital Object Identifier (DOI)https://doi.org/10.1371/journal.pone.0085811
Scopus EID2-s2.0-84924846582
Open accessPublished as ‘gold’ (paid) open access
Research or scholarlyResearch
Page range1-9
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online20 Jan 2014
Publication process dates
Accepted02 Dec 2013
Deposited28 Jul 2021
Permalink -

https://acuresearchbank.acu.edu.au/item/8w6wz/biview-learning-for-human-posture-segmentation-from-3d-points-cloud

Download files


Publisher's version
  • 3
    total views
  • 1
    total downloads
  • 1
    views this month
  • 0
    downloads this month
These values are for the period from 19th October 2020, when this repository was created.

Export as

Related outputs

Diversified Bayesian nonnegative matrix factorization
Qiao, Maoying, Jun,Yu, Tongliang, Liu, Xinchao, Wang and Dacheng, Tao. (2020). Diversified Bayesian nonnegative matrix factorization. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). New York Hilton Midtown, New York, New York, United States of America 07 - 12 Feb 2020 AAAI Press. pp. 5420-5427 https://doi.org/10.1609/aaai.v34i04.5991
Adapting stochastic block models to power-law degree distributions
Qiao, Maoying, Yu, Jun, Bian, Wei, Li, Qiang and Tao, Dacheng. (2019). Adapting stochastic block models to power-law degree distributions. IEEE Transactions on Cybernetics. 49(2), pp. 626-637. https://doi.org/10.1109/TCYB.2017.2783325Y
Diversified dictionaries for multi-instance learning
Qiao, Maoying, Liu, Liu, Yu, Jun, Xu, Chang and Tao, Dacheng. (2017). Diversified dictionaries for multi-instance learning. Pattern Recognition. 64, pp. 407-416. https://doi.org/10.1016/j.patcog.2016.08.026
Improving stochastic block models by incorporating power-law degree characteristic
Qiao, Maoying, Yu, Jun, Bian, Wei, Li, Qiang and Tao, Dacheng. (2017). Improving stochastic block models by incorporating power-law degree characteristic. Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). Melbourne, Australia 19 - 25 Aug 2017 International Joint Conferences on Artificial Intelligence Organization. pp. 2620-2626 https://doi.org/10.24963/ijcai.2017/365
Fast sampling for time-varying determinantal point processes
Qiao, Maoying, Xu, Richard Yi Da, Bian, Wei and Tao, Dacheng. (2016). Fast sampling for time-varying determinantal point processes. ACM Transactions on Knowledge Discovery from Data. 11(1), p. 8. https://doi.org/1556-4681
Conditional graphical lasso for multi-label image classification
Li, Qiang, Qiao, Maoying, Bian, Wei and Tao, Dacheng. (2016). Conditional graphical lasso for multi-label image classification. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, Nevada, United States of America 27 - 30 Jun 2016 Computer Vision Foundation. pp. 2977-2986 https://doi.org/10.1109/CVPR.2016.325
Diversified hidden Markov models for sequential labeling
Qiao, Maoying, Bian, Wei, Da Xu, Richard Yi and Tao, Dacheng. (2015). Diversified hidden Markov models for sequential labeling. IEEE Transactions on Knowledge and Data Engineering. 27(11), pp. 2947-2960. https://doi.org/10.1109/TKDE.2015.2433262