Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait recognition using linear time normalization
Pattern Recognition
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Canonical Correlation Analysis of Video Volume Tensors for Action Categorization and Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tensor Decompositions and Applications
SIAM Review
Multicamera tracking of articulated human motion using shape and motion cues
IEEE Transactions on Image Processing
Discriminant nonnegative tensor factorization algorithms
IEEE Transactions on Neural Networks
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum Relative Margin and Data-Dependent Regularization
The Journal of Machine Learning Research
Spatiotemporal salient points for visual recognition of human actions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of humans using gait
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
Minimum Class Variance Support Vector Machines
IEEE Transactions on Image Processing
Spatiotemporal Localization and Categorization of Human Actions in Unsegmented Image Sequences
IEEE Transactions on Image Processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
MPCA: Multilinear Principal Component Analysis of Tensor Objects
IEEE Transactions on Neural Networks
Subspace Learning from Image Gradient Orientations
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This work addresses the two class classification problem within the tensor-based large margin classification paradigm. To this end, we formulate the higher rank Support Tensor Machines (STMs), in which the parameters defining the separating hyperplane form a tensor (tensorplane) that is constrained to be the sum of rank one tensors. Subsequently, we propose two extensions in which the separating tensorplanes take into consideration the spread of the training data along the different tensor modes. More specifically, we first propose the higher rank @S/@S"w STMs that use the total or the within-class covariance matrix in order to whiten the data and thus provide invariance to affine transformations. Second, we propose the higher rank Relative Margin Support Tensor Machines (RMSTMs) that bound from above the distance of the data samples from the separating tensorplane while maximizing the margin from it. The corresponding optimization problem is solved in an iterative manner utilizing the CANDECOMP/PARAFAC (CP) decomposition, where at each iteration the parameters corresponding to the projections along a single tensor mode are estimated by solving a typical Support Vector Machine (SVM)-type optimization problem. The efficiency of the proposed method is illustrated on the problems of gait and action recognition where we report results that improve, in some cases considerably, the state of the art.