Label localization with weakly spatial constrained graph propagation

Properly utilizing the spatial correlation of regions benefits for improving the performance of label localization task. However, we could not obtain this information directly since we do not have the region level ground truth. In this paper, we propose a weakly spatial constrained graph propagation by mining the spatial correlation from unlabeled regions and integrating it into the graph propagation framework. Our main framework contains two steps: the spatial constrained graph (SCG) construction and label propagation. Firstly, images are over-segmented and each patch is considered as a node. We deem the relatively stable patch combination as a spatial context to construct the SCG, and encourage label propagations where those patches are visually similar as well as spatially consistent. In the second step, we add the dissimilarity constraints and image level label constraints to the label propagation. The propagation procedure is formulated as a constrained optimization problem and it can be efficiently solved by an iteration method. Experiments on three benchmark datasets demonstrate that the spatial correlation mined by our method is effective to the label localization task.