基于稀疏张量判别分析的人体行为识别

doi:10.3969/j.issn.1006-2475.2020.03.023

计算机与现代化

• 图像处理 • 上一篇

基于稀疏张量判别分析的人体行为识别

（河海大学计算机与信息学院，江苏南京210000）

收稿日期:2019-06-15 出版日期:2020-03-24 发布日期:2020-03-30
作者简介:卢雨彤(1995-),女,江苏连云港人,硕士研究生,研究方向:图像分类,行为识别,E-mail: 1010451239@qq.com; 韩立新(1967-),男,江苏南京人,研究员,博士生导师,博士,研究方向：模式识别,数据挖掘。

Human Behavior Recognition Based on Sparse Tensor Discriminant Analysis

（College of Computer and Information, Hohai University, Nanjing 210000， China）

Received:2019-06-15 Online:2020-03-24 Published:2020-03-30

摘要/Abstract

摘要： 在模式识别中，如何在提取关键特征的同时对样本进行降维与识别是研究的热点之一。在局部Fisher判别分析（LFDA）的基础上，结合张量表示和稀疏分析，本文提出一种基于稀疏张量的特征提取方法：稀疏张量局部Fisher判别分析（STLFDA）。该方法把张量局部Fisher判别分析（TLFDA）算法中特征分解问题转化为线性回归问题，并用弹性网络解决线性回归中的特征选择问题，既满足了张量局部Fisher判别分析的目标，又保证了得到的投影矩阵的稀疏性。通过在Weizmann人体行为数据库上的实验，表明了稀疏张量局部Fisher判别分析（STLFDA）算法的有效性。

关键词: 局部Fisher判别分析, 稀疏分析, 张量表示, 弹性网络

Abstract: In pattern recognition, how to reduce the dimension and identify the samples while extracting the key features is one of research hotspots. Based on local Fisher discriminant analysis (LFDA), this paper proposes a feature extraction method combining tensor representation with sparse analysis: Sparse Tensor Local Fishers Discriminant Analysis (STLFDA). This method transforms the feature decomposition problem in tensor local Fisher discriminant analysis (TLFDA) algorithm into linear regression problem, and solves the feature selection problem in linear regression with elastic network. It not only satisfies the goal of the Tensor Local Fisher Discriminant Analysis, but also guarantees the sparsity of the projection matrix. The validity of STLFDA algorithm is demonstrated by experiments on the Weizmann human behavior database.

Key words: local Fisher discriminant analysis, sparse analysis, tensor representation, elastic network

中图分类号:

TP391.4

卢雨彤，韩立新. 基于稀疏张量判别分析的人体行为识别[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2020.03.023.

LU Yu-tong, HAN Li-xin. Human Behavior Recognition Based on Sparse Tensor Discriminant Analysis[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2020.03.023.

参考文献

［1］黄修武,郭跃飞,杨静宇. 基于代数方法的图像特征抽取和识别［J］. 南京理工大学学报(自然科学版), 1998,22(1):1-5.
［2］ WOLD S, ESBENSEN K, GELADI P. Principal component analysis［J］. Chemometrics and Intelligent Laboratory Systems, 1987,2(1-3):37-52.
［3］ BELHUMEUR P N, HESPANHA J P, KRIEGMAN D J. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection［C］// Proceedings of the 4th European Conference on Computer Vision. 1997:45-58.
［4］ HE X F, YAN S C, HU Y X, et al. Face recognition using laplacianfaces［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2005,37(3):328-340.
［5］ SUGIYAMA M. Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis［J］. Journal of Machine Learning Research, 2007,8:1027-1061.
［6］ HE X, CAI D, NIYOGI P. Tensor subspace analysis［C］// Annual Conference on Neural Information Processing Systems. 2005:499-506.
［7］ LU H P, PLATANIOTIS K N K, VENETSANOPOULOS A N. MPCA: Multilinear principal component analysis of tensor objects［J］. IEEE Transactions on Neural Networks, 2008,19(1):18-39.
［8］ YAN S C, XU D, YANG Q, et al. Multilinear discriminant analysis for face recognition［J］. IEEE Transactions on Image Processing, 2007,16(1):212-220.〖HJ1.5mm〗
［9］ ZHANG Z, CHOW W S. Tensor locally linear discriminative analysis［J］. IEEE Signal Processing Letters, 2011,18(11):643-646.
［10］LE PENNEC E, MALLAT S. Sparse geometric image representations with bandelets［J］. IEEE Transactions on Image Processing, 2005,14(4):423-438.
［11］ZOU H, HASTIE T, TIBSHIRANI R. Sparse principal component analysis［J］. Journal of Computational and Graphical Statistics, 2006,15(2):265-286.
［12］CLEMMENSEN L, HASTIE T, WITTEN D, et al. Sparse discriminant analysis［J］. Technometrics, 2011,53(4):406-413.
［13］QIAO L S, CHEN S C, TAN X Y. Sparsity preserving projections with applications to face recognition［J］. Pattern Recognition, 2010,43(1):331-341.
［14］LAI Z H, XU Y, CHEN Q C, et al. Multilinear sparse principal component analysis［J］. IEEE Transactions on Neural Networks and Learning Systems, 2014,25(10):1942-1950.
［15］LAI Z H, XU Y, YANG J, et al. Sparse tensor discriminant analysis［J］. IEEE Transactions on Image Processing, 2013,22(10):3904-3915.
［16］LAI Z H, WAN M H, JIN Z, et al. Sparse two-dimensional local discriminant projections for feature extraction［J］. Neurocomputing, 2011,74(4):629-637.
［17］徐光祐,曹媛媛. 动作识别与行为理解综述［J］. 中国图象图形学报A, 2009,14(2):189-195.
［18］谷军霞,丁晓青,王生进. 行为分析算法综述［J］. 中国图象图形学报A, 2009,14(3):377-387.
［19］〖JP3〗WANG L, SUTER D. Learning and matching of dynamic shape manifolds for human action recognition［J］. IEEE Transactions on Image Processing, 2007,16(6):1646-1661.
［20］WANG L, SUTER D. Recognizing human activities from silhouettes: Motion subspace and factorial discriminative graphical model［C］// 2007 IEEE Conference on Computer Vision and Pattern Recognition. 2007:1-8.
［21］JIA K, YEUNG D Y. Human action recognition using local spatio-temporal discriminant embedding［C］// 2008 IEEE Conference on Computer Vision and Pattern Recognition. 2008:1-8.
［22］KOLDA T G, BADER B W. Tensor decompositions and applications［J］. SIAM Review, 2009,51(3):455-500.
［23］JOLLIFFE I T, TRENDAFILOV N T, UDDIN M. A modified principal component technique based on the LASSO［J］. Journal of Computational and Graphical Statistics, 2003,12(3):531-547.
［24］ZOU H, HASTIE T. Regression shrinkage and selection via the elastic net, with applications to microarrays［J］. Journal of the Royal Statistical Society Series B, 2005,67:301-320.

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基于稀疏张量判别分析的人体行为识别

Human Behavior Recognition Based on Sparse Tensor Discriminant Analysis

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