Discrete-Continuous Gradient Orientation Estimation for Faster Image Segmentation

Michael Donoser, Dieter Schmalstieg; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 3158-3165

The state-of-the-art in image segmentation builds hierarchical segmentation structures based on analyzing local feature cues in spectral settings. Due to their impressive performance, such segmentation approaches have become building blocks in many computer vision applications. Nevertheless, the main bottlenecks are still the computationally demanding processes of local feature processing and spectral analysis. In this paper, we demonstrate that based on a discrete-continuous optimization of oriented gradient signals, we are able to provide segmentation performance competitive to state-of-the-art on BSDS 500 (even without any spectral analysis) while reducing computation time by a factor of 40 and memory demands by a factor of 10.