Fast multiresolution image querying
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Component-Based Face Recognition with 3D Morphable Models
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Information Theory in Computer Vision and Pattern Recognition
Information Theory in Computer Vision and Pattern Recognition
Hi-index | 0.00 |
The paper proposes various approaches to classifying sign-based representations of images based on distance functions. Any image is represented as a set of features describing differences in brightness. The construction of a distance function is proposed using classical functionals of information theory: the Shannon entropy and the Kullback-Leibler distance. It is shown that the Bayes classification in the case of independent features can be also described by distance functions. In the last section, the proposed approaches are evaluated using a face detection problem.