采用多任务稳健主成分分析的运动目标分割

doi:10.3969/j.issn.0255-8297.2014.05.007

应用科学学报 ›› 2014, Vol. 32 ›› Issue (5): 473-480.doi: 10.3969/j.issn.0255-8297.2014.05.007

• RESEARCHNOTES • 上一篇下一篇

采用多任务稳健主成分分析的运动目标分割

王向阳1,2, 万旺根1,2

1. 上海大学通信与信息工程学院，上海200444
2. 上海大学智慧城市研究院，上海200444

出版日期:2014-09-23 发布日期:2014-09-23
作者简介:WANG Xiang-yang, Ph.D., associate professor, research interests including machine learning, image processing, pattern recognition, computer vision, E-mail: wangxiangyang@shu.edu.cn; WAN Wang-gen, Ph.D., professor, research interests including computer graphics, data visualization and data mining, E-mail: wanwg@staff.shu.edu.cn
基金资助:
the National Natural Science Foundation of China (No.60975024, No.61373084); the Shanghai Natural
Science Foundation (No.09ZR1412300); the “863” National High Technology Research and Development Program of China
(No.2013AA01A603)

Motion Segmentation via Multi-task Robust Principal Component Analysis

WANG Xiang-yang1,2, WAN Wang-gen1,2

1. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
2. Institute of Smart City, Shanghai University, Shanghai 200444, China

Online:2014-09-23 Published:2014-09-23

摘要/Abstract

摘要： 提出一种多任务稳健主成分分析方法，用以结合多视觉特征实现运动目标分割. 给定由多类型特征矩阵描述的视频数据，将它分解为低秩和稀疏部分，其中的稀疏部分对应于运动目标. 该矩阵分解过程是一个凸优化问题，通过用ALM方法最小化核范数和`2,1-范数的约束组合. 与仅利用单类型特征的方法相比，本文提出的方法能够结合多类型特征，因此可获得更加精确可靠的结果. 对HumanEva和Change Detection两个数据集的实验表明了该方法的有效性.

关键词: 运动分割, 低秩矩阵恢复, 稀疏表示, 稳健主成分分析, 增广拉格朗日乘子法

Abstract: This paper proposes a new algorithm, multi-task robust principal component analysis (MTRPCA),to collaboratively integrate multiple visual features for motion segmentation. Given the video data described by multiple features, the motion parts are obtained by jointly decomposing multiple feature matrices into pairs of low-rank and sparse matrices. The inference process is formulated as a convex optimization problem that minimizes a constrained combination of nuclear norm and `2,1-norm. The convex optimization problem can be solved efficiently with an augmented Lagrange multiplier (ALM) method. Compared with previous methods based on individual features, the proposed method seamlessly integrates multiple features within a single inference
step, and thus produces more accurate and reliable results. Experiments on human motion data sets, Human Eva and change detection, show that the proposed MTRPCA is effective and promising.

Key words: motion segmentation, low-rank matrix recovery, sparse representation, robust principal component analysis, augmented Lagrange multipliers

中图分类号:

TP181

王向阳1,2, 万旺根1,2. 采用多任务稳健主成分分析的运动目标分割[J]. 应用科学学报, 2014, 32(5): 473-480.

WANG Xiang-yang1,2, WAN Wang-gen1,2. Motion Segmentation via Multi-task Robust Principal Component Analysis[J]. Journal of Applied Sciences, 2014, 32(5): 473-480.

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采用多任务稳健主成分分析的运动目标分割

Motion Segmentation via Multi-task Robust Principal Component Analysis

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