Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel independent component analysis
The Journal of Machine Learning Research
Improvements on CCA model with application to face recognition
Intelligent information processing II
Locality preserving CCA with applications to data visualization and pose estimation
Image and Vision Computing
Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Multiple feature fusion by subspace learning
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A new method of feature fusion and its application in image recognition
Pattern Recognition
A novel feature fusion method based on partial least squares regression
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
A shape- and texture-based enhanced Fisher classifier for face recognition
IEEE Transactions on Image Processing
Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data
IEEE Transactions on Image Processing
A Kernel Approach for Semisupervised Metric Learning
IEEE Transactions on Neural Networks
Multi-resolution feature fusion for face recognition
Pattern Recognition
Hi-index | 0.01 |
Multiset canonical correlation analysis (MCCA) is difficult to effectively express the integrated correlation among multiple feature vectors in feature fusion. Thus, this paper firstly presents a novel multiset integrated canonical correlation analysis (MICCA) framework. The MICCA establishes a discriminant correlation criterion function of multi-group variables based on generalized correlation coefficient. The criterion function can clearly depict the integrated correlation among multiple feature vectors. Then the paper presents a multiple feature fusion theory and algorithm using the MICCA method. The detailed process of the algorithm is as follows: firstly, extract multiple feature vectors from the same patterns by using different feature extraction methods; then extract multiset integrated canonical correlation features using MICCA; finally form effective discriminant feature vectors through two given feature fusion strategies for pattern classification. The multi-group feature fusion method based on MICCA not only achieves the aim of feature fusion, but also removes the redundancy between features. The experiment results on CENPARMI handwritten Arabic numerals and UCI multiple features database show that the MICCA method has better recognition rates and robustness than the fusion methods based on canonical correlation analysis (CCA) and MCCA.