Weakly supervised semantic segmentation using object level and context level information

In this paper, a new approach to weakly supervised semantic segmentation is proposed. The main goal in semantic segmentation is to assign a semantic label to each pixel. In weakly supervised setting, each training image is only labeled by the classes they contain, not by their locations. The main contribution of this paper is to simultaneously incorporate the object level and context level information in assigning class label to each pixel of the image. To do this, regions in each image are grouped such that groups of regions in images with the same semantic label have the same appearance and context. To do this, an iterative move-making algorithm is proposed. At first, each pixel of the image is initially labeled and then model of appearance and context for each class label is learned. Then, semantic label of each pixel is updated such that the regions with the same sematic label have the same appearance and context in the set of images. In the next step, appearance and context models for each semantic class are updated. It is repeated until in the two consecutive epochs, labels of the pixels are not changed. To evaluate our proposed approach, it is applied on the MSRC dataset. The obtained results show that our approach outperforms comparable state-of-the-art approaches.