Learning Separable Filters with Shared Parts
Learned image features can provide great accuracy in many Computer Vision tasks. However, when the convolution filters used to learn image features are numerous and not separable, feature extraction becomes computationally demanding and impractical to use in real-world situations. In this thesis work, a method for learning a small number of separable filters to approximate an arbitrary non-separable filter bank is developed. In this approach, separable filters are learned by grouping the arbitrary filters into a tensor and optimizing a tensor decomposition problem. The separable filter learning with tensor decomposition is general and can be applied to generic filter banks to reduce the computational burden of convolutions without a loss in performance. Moreover, the proposed approach is orders of magnitude faster than the approach of a recent studies based on l1-norm minimization.