Recursive estimation of motion parameters
Computer Vision and Image Understanding
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An eigenspace update algorithm for image analysis
Graphical Models and Image Processing
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Journal of Cognitive Neuroscience
Efficient Calculation of Primary Images from a Set of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partial eigenvalue decomposition of large images using spatial temporal adaptive method
IEEE Transactions on Image Processing
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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A new incremental online feature extraction approach is proposed based on principal component analysis in conjunction with perturbation theory which for its validity requires the perturbation parameter to be small. Our approach is found to be computationally more efficient in comparison to batch method which for its applicability requires simultaneous availability of all observations for computation of features. It is found on the basis of numerical experiments that the results based on our approach besides being in good agreement with the batch method and other incremental methods are also computationally more efficient. To demonstrate the efficacy of the proposed scheme, experiments have been performed on randomly generated datasets as well as on low and high dimensional datasets, i.e. UCI and face datasets which are available in public domain.