An Efficient Approach to Semantic Segmentation

摘要

We consider the problem of semantic segmentation, i.e. assigning each pixel in an image to a set of pre-defined semantic object categories. State-of-the-art semantic segmentation algorithms typically consist of three components: a local appearance model, a local consistency model and a global consistency model. These three components are generally integrated into a unified probabilistic framework. While it enables at training time a joint estimation of the model parameters and while it ensures at test time a globally consistent labeling of the pixels, it also comes at a high computational cost.