Learning to relate images: Mapping units, complex cells and simultaneous eigenspaces
Abstract
A fundamental operation in many vision tasks, including motion understanding, stereopsis, visual odometry, or invariant recognition, is establishing correspondences between images or between images and data from other modalities. We present an analysis of the role that multiplicative interactions play in learning such correspondences, and we show how learning and inferring relationships between images can be viewed as detecting rotations in the eigenspaces shared among a set of orthogonal matrices. We review a variety of recent multiplicative sparse coding methods in light of this observation. We also review how the squaring operation performed by energy models and by models of complex cells can be thought of as a way to implement multiplicative interactions. This suggests that the main utility of including complex cells in computational models of vision may be that they can encode relations not invariances.
Cite
@article{arxiv.1110.0107,
title = {Learning to relate images: Mapping units, complex cells and simultaneous eigenspaces},
author = {Roland Memisevic},
journal= {arXiv preprint arXiv:1110.0107},
year = {2012}
}
Comments
Revised argument in sections 4 and 3.3. Added illustration of subspaces (Figure 13). Added inference Equation (Eq. 17)