A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis with Tensor Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Image clustering with tensor representation
Proceedings of the 13th annual ACM international conference on Multimedia
Maximum unfolded embedding: formulation, solution, and application for image clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Tensor distance based multilinear multidimensional scaling for image and video analysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Robust Tensor Analysis With L1-Norm
IEEE Transactions on Circuits and Systems for Video Technology
Learning frame relevance for video classification
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Semi-supervised manifold ordinal regression for image ranking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Hi-index | 0.00 |
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize the distance preserving, we propose a novel algorithm called maximum distance embedding (MDE), which aims to maximize the distances between some particular pairs of data points, with the intention of flattening the local nonlinearity and keeping the discriminant information simultaneously in the embedded feature space. Moreover, MDE measures the dissimilarity between data points using L1-norm distance, which is more robust to outliers than widely used Frobenius norm distance. To adapt the nature tensor structure of image and video data, we further propose the multilinear MDE (M2DE). Experiments on various datasets demonstrate that both MDE and M2DE achieve impressive embedding results of image and video data for classification tasks.