Sciweavers

Share
CVPR
2012
IEEE

Learning rotation-aware features: From invariant priors to equivariant descriptors

9 years 2 months ago
Learning rotation-aware features: From invariant priors to equivariant descriptors
Identifying suitable image features is a central challenge in computer vision, ranging from representations for lowlevel to high-level vision. Due to the difficulty of this task, techniques for learning features directly from example data have recently gained attention. Despite significant benefits, these learned features often have many fewer of the desired invariances or equivariances than their hand-crafted counterparts. While translation in-/equivariance has been addressed, the issue of learning rotation-invariant or equivariant representations is hardly explored. In this paper we describe a general framework for incorporating invariance to linear image transformations into product models for feature learning. A particular benefit is that our approach induces transformation-aware feature learning, i.e. it yields features that have a notion with which specific image transformation they are used. We focus our study on rotation in-/equivariance and show the advantages of our app...
Uwe Schmidt, Stefan Roth
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Uwe Schmidt, Stefan Roth
Comments (0)
books