Accurate semantic image labeling by fast Geodesic Propagation
Motivated by recently raised image semantic labeling problem, this paper studies a fast Geodesic Propagation (GP) algorithm that integrates recognition proposal and image compatibility into a graphical representation. Given the recognition proposal map of the image, the initial seeds are selected as confident pixels standing on local proposal peaks by Mean-shift algorithm. The geodesic distance is then defined on a hybrid manifold, combining the color and boundary features with the recognition proposal map. Based on the geodesic distance, the semantic labeling is simultaneously propagated from the initial seeds of all classes to the rest of image pixels. This inference algorithm is capable of multi-labeling an image of 2-mega pixels in one second (with a common PC). In the experiment, we test on 21 generic semantic categories (sky, road, grass …) on MSRC dataset, and 17 categories on LHI dataset to evaluate the performance.