The nature of statistical learning theory
The nature of statistical learning theory
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-linear Bayesian Image Modelling
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Mixtures of Local Linear Subspaces for Face Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Nonlinear manifold learning for visual speech recognition
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Journal of Cognitive Neuroscience
ACM Transactions on Graphics (TOG)
Clustered Blockwise PCA for Representing Visual Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
PROSOPO - a face image synthesis system
PCI'01 Proceedings of the 8th Panhellenic conference on Informatics
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Eigensegments combine image segmentation and Principal Component Analysis (PCA) to obtain a spatio-temporal decomposition of an ensemble of images. The image plane is spatially decomposed into temporally correlated regions. Each region is independently decomposed temporally using PCA. Thus, each image is modeled by several low-dimensional segment-spaces, instead of a single high-dimensional image-space. Experiments show the proposed method gives better classification results, gives smaller reconstruction errors, can handle local changes in appearance and is faster to compute. Results for faces and vehicles are shown.