Supervised Semantic Gradient Extraction Using Linear-Time Optimization
Shulin Yang, Jue Wang, Linda G. Shapiro · 2013
This paper proposes a new supervised semantic edge and gradient extraction approach, which allows the user to roughly scribble over the desired region to extract semantically-dominant and coherent edges in it. Our approach first extracts low-level edge lets (small edge clusters) from the input image as primitives and build a graph upon them, by jointly considering both the geometric and appearance compatibility of edge lets. Given the characteristics of the graph, it cannot be effectively optimized by commonly-used energy minimization tools such as graph cuts. We thus propose an efficient linear algorithm for precise graph optimization, by taking advantage of the special structure of the graph. %Optimal parameter settings of the model are learnt from a dataset. Objective evaluations show that the proposed method significantly outperforms previous semantic edge detection algorithms. Finally, we demonstrate the effectiveness of the system in various image editing tasks.