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

: Schmidt, Uwe; Roth, Stefan


IEEE Computer Society:
IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 : Providence, Rhode Island, USA, 16 - 24 June 2012
New York, NY: IEEE, 2012
ISBN: 978-1-4673-1226-4 (Print)
ISBN: 978-1-4673-1228-8
ISBN: 978-1-4673-1227-1 (Online)
Conference on Computer Vision and Pattern Recognition (CVPR) <30, 2012, Providence/RI>
Conference Paper
Fraunhofer IGD ()
computer vision; machine learning; feature; image restoration; object detection; object recognition; transformation; rotation; Markov random fields (MRF); Forschungsgruppe Visual Inference (VINF)

Identifying suitable image features is a central challenge in computer vision, ranging from representations for low-level 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 approach in learning rotation-invariant image priors and in building rotation-equivariant and invariant descriptors of learned features, which result in state-of-the-art performance for rotation-invariant object detection.