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- W2107011987 abstract "Many natural image sets are samples of a low-dimensional manifold in the space of all possible images. When the image data set is not a linear combination of a small number of basis images, linear dimensionality reduction techniques such as PCA and ICA fail and non-linear dimensionality reduction techniques are required to automatically determine the intrinsic structure of the image set. Recent techniques such as ISOMAP and LLE provide a mapping between the images and a low-dimensional parameterization of the images. This paper specializes general manifold learning by considering a small set of image distance measures that correspond to key transformation groups observed in natural images. This results in more meaningful embeddings for a variety of applications." @default.
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- W2107011987 date "2007-03-01" @default.
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- W2107011987 title "Image distance functions for manifold learning" @default.
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- W2107011987 doi "https://doi.org/10.1016/j.imavis.2006.01.016" @default.
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