Representing Images Using Nonorthogonal Haar-Like Bases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Tensor Approximation of Multi-Dimensional Visual Data
IEEE Transactions on Visualization and Computer Graphics
Compression of facial images using the K-SVD algorithm
Journal of Visual Communication and Image Representation
Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Sparse Super Symmetric Tensor Factorization
Neural Information Processing
Flexible Component Analysis for Sparse, Smooth, Nonnegative Coding or Representation
Neural Information Processing
Nonnegative Tensor Factorization with Smoothness Constraints
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Computing non-negative tensor factorizations
Optimization Methods & Software - Mathematical programming in data mining and machine learning
Probabilistic polyadic factorization and its application to personalized recommendation
Proceedings of the 17th ACM conference on Information and knowledge management
iOLAP: A framework for analyzing the internet, social networks, and other networked data
IEEE Transactions on Multimedia - Special section on communities and media computing
Discriminant nonnegative tensor factorization algorithms
IEEE Transactions on Neural Networks
Multiresolution approach in computing NTF
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Hierarchical ALS algorithms for nonnegative matrix and 3D tensor factorization
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Projective nonnegative graph embedding
IEEE Transactions on Image Processing
FacetCube: a framework of incorporating prior knowledge into non-negative tensor factorization
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Tensor sparse coding for region covariances
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Non-negative tensor factorization applied to music genre classification
IEEE Transactions on Audio, Speech, and Language Processing
A survey of multilinear subspace learning for tensor data
Pattern Recognition
Controlling sparseness in non-negative tensor factorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Task specific factors for video characterization
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Tensor based sparse decomposition of 3D shape for visual detection of mirror symmetry
Computer Methods and Programs in Biomedicine
A tensor factorization based least squares support tensor machine for classification
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
| Hi-index | 0.00 |
We introduce an algorithm for a non-negative 3D tensor factorization for the purpose of establishing a local parts feature decomposition from an object class of images. In the past such a decomposition was obtained using non-negative matrix factorization (NMF) where images were vectorized before being factored by NMF. A tensor factorization (NTF) on the other hand preserves the 2D representations of images and provides a unique factorization (unlike NMF which is not unique). The resulting "factors" from the NTF factorization are both sparse (like with NMF) but also separable allowing efficient convolution with the test image. Results show a superior decomposition to what an NMF can provide on all fronts 驴 degree of sparsity, lack of ghost residue due to invariant parts and efficiency of coding of around an order of magnitude better. Experiments on using the local parts decomposition for face detection using SVM and Adaboost classifiers demonstrate that the recovered features are discriminatory and highly effective for classification.