We propose a simple approach to semantic image segmentation. Our system scores low-level patches according to their class relevance, propagates these posterior probabilities to pixels and uses low-level segmentation to guide the semantic segmentation. The two main contributions of this paper are as follows. First, for the patch scoring, we describe each patch with a high-level descriptor based on the Fisher kernel and use a set of linear classifiers. While the Fisher kernel methodology was shown to lead to high accuracy for image classification, it has not been applied to the segmentation problem. Second, we use global image classifiers to take into account the context of the objects to be segmented. If an image as a whole is unlikely to contain an object class, then the corresponding class is not considered in the segmentation pipeline. This increases the classification accuracy and reduces the computational cost. We will show that despite its apparent simplicity, this system provides above state-of-the-art performance on the PASCAL VOC 2007 dataset and state-of-the-art performance on the MSRC 21 dataset.
Csurka et al. REF proposed a simple framework for semantic segmentation: a Fisher kernel derives high-level descriptors for computing class relevance on the patch level, while the context is inferred by classification at the image level.
A simple high performance approach to semantic segmentation
In BMVC
Computer Science