Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
A generalization of dissimilarity representations using feature lines and feature planes
Pattern Recognition Letters
A Dynamic Programming Technique for Optimizing Dissimilarity-Based Classifiers
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Similarity-based classification of sequences using hidden Markov models
Pattern Recognition
Image dissimilarity-based quantification of lung disease from CT
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
On improving dissimilarity-based classifications using a statistical similarity measure
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
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This paper presents an empirical evaluation on a dissimilarity measure strategy by which dissimilarity-based classifications (DBCs) [10] can be efficiently implemented. In DBCs, classifiers are not based on the feature measurements of individual objects, but rather on a suitable dissimilarity measure among the objects. In image classification tasks, however, one of the most intractable problems to measure the dissimilarity is the distortion and lack of information caused by the differences in illumination and directions and outlier data. To overcome this problem, in this paper, we study a new way of performing DBCs in eigenspaces spanned, one for each class, by the subset of principal eigenvectors, extracted from the training data set through a principal component analysis. Our experimental results, obtained with well-known benchmark databases, demonstrate that when the dimensionality of the eigenspaces has been appropriately chosen, the DBCs can be improved in terms of classification accuracies.