Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
A Linear Time Histogram Metric for Improved SIFT Matching
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Multiclass probabilistic kernel discriminant analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Separable linear discriminant classification
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
IEEE Transactions on Image Processing
Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
We propose signature linear discriminant analysis (signature-LDA) as an extension of LDA that can be applied to signatures, which are known to be more informative representations of local image features than vector representations, such as visual word histograms. Based on earth mover's distances between signatures, signature-LDA does not require vectorization of local image features in contrast to LDA, which is one of the main limitations of classical LDA. Therefore, signature-LDA minimizes the loss of intrinsic information of local image features while selecting more discriminating features using label information. Empirical evidence on texture databases shows that signature-LDA improves upon state-of-the-art approaches for texture image classification and outperforms other feature selection methods for local image features.