Learning Smooth Pattern Transformation Manifolds

TL;DR

This paper introduces methods for learning smooth pattern transformation manifolds from image sets, enabling accurate data approximation and classification while maintaining invariance to geometric transformations.

Contribution

It proposes a greedy and DC optimization-based approach for constructing transformation manifolds and extends it to multiple classes for improved classification accuracy.

Findings

High accuracy in data approximation and classification.

Effective invariance to geometric transformations.

Demonstrated applicability to rotation, translation, and scaling.

Abstract

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. In order 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 (Difference-of-Convex) optimization scheme that is applicable to a wide range of transformation and dictionary models, and demonstrate its application to transformation manifolds generated by 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. Experimental results suggest that the proposed methods yield high accuracy in the approximation and classification of data compared to some reference methods, while the invariance to geometric transformations is achieved due to the transformation manifold model.