  Deep Convolutional Neural Networks (DCNNs) are capable of obtaining powerful
image representations, which have attracted great attentions in image
recognition. However, they are limited in modeling orientation transformation
by the internal mechanism. In this paper, we develop Orientation Convolution
Networks (OCNs) for image recognition based on the proposed Landmark Gabor
Filters (LGFs) that the robustness of the learned representation against
changed of orientation can be enhanced. By modulating the convolutional filter
with LGFs, OCNs can be compatible with any existing deep learning networks.
LGFs act as a Gabor filter bank achieved by selecting $ p $ $ \left( \ll
n\right) $ representative Gabor filters as andmarks and express the original
Gabor filters as sparse linear combinations of these landmarks. Specifically,
based on a matrix factorization framework, a flexible integration for the local
and the global structure of original Gabor filters by sparsity and low-rank
constraints is utilized. With the propogation of the low-rank structure, the
corresponding sparsity for representation of original Gabor filter bank can be
significantly promoted. Experimental results over several benchmarks
demonstrate that our method is less sensitive to the orientation and produce
higher performance both in accuracy and cost, compared with the existing
state-of-art methods. Besides, our OCNs have few parameters to learn and can
significantly reduce the complexity of training network.
