We present and discuss the techniques used in applying the singular value decomposition (SVD) in order to resolve such difficult problems as object and feature extraction of images in the context where the number of images and related data to characterize them become very large.
We will present preliminary results indicating the potential of such methods in analyzing data pertinent to large image collections where image complexity is high and where detailed ,exact and realistic physical models of the complexities are yet to be written. The SVD can be seen as an empirical type of alternative to such
physical modeling .