Face Recognition: The Problem of Compensating for Changes in Illumination Direction
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
On the Euclidean Distance of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
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
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
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In dissimilarity-based classifications (DBCs), classifiers are not based on the feature measurements of individual objects, but rather on a suitable dissimilarity measure among the objects. In this paper, we study a new way of measuring the dissimilarity between two object images using a SIFT (Scale Invariant Feature Transformation) algorithm [5], which transforms image data into scale-invariant coordinates relative to local features based on the statistics of gray values in scalespace. With this method, we find an optimal or nearly optimal matching among differing images in scaling and rotation, which leads us to obtain dissimilarity representation after matching them. Our experimental results, obtained with wellknown benchmark databases, demonstrate that the proposed mechanism works well and, compared with the previous approaches, achieves further improved results in terms of classification accuracy.