Semantic Filtering
Edge preserving image operations aim at smoothing an image without blurring the edges. Many excellent edge preserving filtering techniques have been proposed recently to reduce the computational complexity or/and separate different scale structures. They normally adopt a user selected scale measurement to control the detail/texture smoothing. However, natural photos contain objects of different sizes which cannot be described by a single scale measurement. On the other hand, edge/contour detection/ analysis is closely related to edge preserving filtering and has achieved significant progress recently. Nevertheless, most of the state of the art filtering techniques ignore the success in this area. Inspired by the fact that learning based edge detectors/classifiers significantly outperform traditional manually designed detectors, this paper proposes a learning based edge preserving filtering technique. It synergistically combines the efficiency of the recursive filter and the effectiveness of the recent edge detector for scale aware edge preserving filtering. Unlike previous filtering methods, the propose filter can efficiently extract subjectively meaningful structures from natural scenes containing multiple scale objects.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)