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Learning Smooth Pattern Transformation Manifolds
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Date
2013-04-01
Author
Vural, Elif
Frossard, Pascal
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Manifold models provide low-dimensional representations that are useful for processing and analyzing data in a transformation-invariant way. In this paper, we study the problem of learning smooth pattern transformation manifolds from image sets that represent observations of geometrically transformed signals. To construct a manifold, we build a representative pattern whose transformations accurately fit various input images. We examine two objectives of the manifold-building problem, namely, approximation and classification. For the approximation problem, we propose a greedy method that constructs a representative pattern by selecting analytic atoms from a continuous dictionary manifold. We present a dc optimization scheme that is applicable to a wide range of transformation and dictionary models, and demonstrate its application to the transformation manifolds generated by the rotation, translation, and anisotropic scaling of a reference pattern. Then, we generalize this approach to a setting with multiple transformation manifolds, where each manifold represents a different class of signals. We present an iterative multiple-manifold-building algorithm such that the classification accuracy is promoted in the learning of the representative patterns. The experimental results suggest that the proposed methods yield high accuracy in the approximation and classification of data compared with some reference methods, while the invariance to geometric transformations is achieved because of the transformation manifold model.
Subject Keywords
Manifold learning
,
Pattern transformation manifolds
,
Pattern classification
,
Transformation-invariance
,
Sparse approximations
URI
https://hdl.handle.net/11511/35066
Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
DOI
https://doi.org/10.1109/tip.2012.2227768
Collections
Department of Electrical and Electronics Engineering, Article