A Fragment-Based Approach to Object Representation and Classification
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Object Recognition with Informative Features and Linear Classification
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The equivalence of two-dimensional PCA to line-based PCA
Pattern Recognition Letters
Volume measure in 2DPCA-based face recognition
Pattern Recognition Letters
A Novel Hybrid Approach Based on Sub-pattern Technique and Extended 2DPCA for Color Face Recognition
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
Letters: Laplacian bidirectional PCA for face recognition
Neurocomputing
A survey of multilinear subspace learning for tensor data
Pattern Recognition
BDPCA plus LDA: a novel fast feature extraction technique for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.10 |
In order to extract low-dimensional features from image data, matrix-based subspace methods such as 2DPCA and tensor PCA have been recently proposed. Since these methods extract features based on 2D image matrices rather than 1D vectors, they can preserve useful information in image matrices and we can expect better classification performance by using the matrix features. In order to maximize the advantages of the matrix features, it is also important to use an appropriate similarity measure between two feature matrices. This paper proposes a method for learning similarity measures for feature matrices, which utilizes distribution properties of given data set and class membership. Through computational experiments with facial image data, we confirm that the obtained similarity measure by the proposed method can give better classification performance than conventional similarity measures for matrix data.