Weakly Supervised Semantic Segmentation by Multiple Group Cosegmentation

Weakly supervised semantic segmentation aims at segmenting images by image-level labels. The existing methods try to train an end-to-end CNN network, which needs to handle multiple classes that is difficult. In addition, the existing methods are sensitive to the image-level cues such as discriminative regions and the pseudo-annotations. To avoid these drawbacks, this paper proposes a new strategy, which first obtains the foregrounds of each class by multiple group cosegmentation, and then combines the results to form the semantic segmentation. In our method, three new aspects are considered. (1) we solve semantic segmentation by each class that is easy to handle. (2) we extract discriminative regions more globally by context analysis. (3) we learn local-to-global segmentation network to segment the object from local discriminative priors. A new CNN network for multiple group cosegmentation is proposed. Two subnetworks such as global context based discriminative region extraction network and local-to-global segmentation network are designed. A simple combination method based on the discriminative map is proposed to finally obtain the semantic segmentation results. We verify the proposed method on Pascal VOC dataset. The experimental results show that the proposed method can obtain mIOU value 0.563 and 0.603 (without CRF post-processing) on the validation and test dataset that outperforms many existing weakly supervised semantic segmentation methods.