Abstract

Image semantic segmentation is the task of partitioning image into several regions based on semantic concepts. In this paper, we learn a weakly supervised semantic segmentation model from social images whose labels are not pixellevel but image-level; furthermore, these labels might be noisy. We present a joint conditional random fifield model leveraging various contexts to address this issue. More specififically, we extract global and local features in multiple scales by convolutional neural network and topic model. Inter-label correlations are captured by visual contextual cues and label co-occurrence statistics. The label consistency between image-level and pixel-level is fifinally achieved by iterative refifinement. Experimental results on two realworld image datasets PASCAL VOC2007 and SIFT-Flow demonstrate that the proposed approach outperforms stateof-the-art weakly supervised methods and even achieves accuracy comparable with fully supervised methods