Region Merging Driven by Deep Learning for RGB-D Segmentation and Labeling

Among the various segmentation techniques, a widely used family of approaches are the ones based on region merging, where an initial oversegmentation is progressively refined by joining segments with similar characteristics. Instead of using deterministic approaches to decide which segments are going to be merged we propose to exploit a convolutional neural network which takes a couple of segments as input and decides whether to join or not the segments. We fitted this idea into an existent iterative semantic segmentation scheme for RGB-D data. We were able to lower the number of free parameters and to greatly speedup the procedure while achieving comparable or even higher results, thus allowing for its usage in free navigation systems. Furthermore, our method could be extended straightforwardly to other fields where region merging strategies are exploited.