Weighted and robust incremental method for subspace learning
- 1 January 2003
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1494-1501 vol.2
- https://doi.org/10.1109/iccv.2003.1238667
Abstract
Visual learning is expected to be a continuous and robust process, which treats input images and pixels selectively. In this paper, we present a method for subspace learning, which takes these considerations into account. We present an incremental method, which sequentially updates the principal subspace considering weighted influence of individual images as well as individual pixels within an image. This approach is further extended to enable determination of consistencies in the input data and imputation of the values in inconsistent pixels using the previously acquired knowledge, resulting in a novel incremental, weighted and robust method for subspace learning.Keywords
This publication has 17 references indexed in Scilit:
- Parameterisation of a stochastic model for human face identificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A framework for modeling the appearance of 3D articulated figuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Shot boundary detection using temporal statistics modelingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A Robust PCA Algorithm for Building Representations from Panoramic ImagesLecture Notes in Computer Science, 2002
- A Bayesian computer vision system for modeling human interactionsIeee Transactions On Pattern Analysis and Machine Intelligence, 2000
- Sequential Karhunen-Loeve basis extraction and its application to imagesIEEE Transactions on Image Processing, 2000
- An Eigenspace Update Algorithm for Image AnalysisGraphical Models and Image Processing, 1997
- Principal component analysis with missing data and its application to polyhedral object modelingIeee Transactions On Pattern Analysis and Machine Intelligence, 1995
- Robust principal component analysis by self-organizing rules based on statistical physics approachIEEE Transactions on Neural Networks, 1995
- Efficient Calculation of Primary Images from a Set of ImagesIeee Transactions On Pattern Analysis and Machine Intelligence, 1982