As time goes by-anytime semantic segmentation with iterative context forests

Abstract

We present a new approach for contextual semantic segmentation and introduce a new tree-based framework, which combines local information and context knowledge in a single model. The method itself is also suitable for anytime classification scenarios, where the challenge is to estimate a label for each pixel in an image while allowing an interruption of the estimation at any time. This offers the application of the introduced method in time-critical tasks, like automotive applications, with limited computational resources unknown in advance. Label estimation is done in an iterative manner and includes spatial context right from the beginning. Our approach is evaluated in extensive experiments showing its state-of-the-art performance on challenging street scene datasets with anytime classification abilities. © 2012 Springer-Verlag.