  Current successful approaches for generic (non-semantic) segmentation rely
mostly on edge detection and have leveraged the strengths of deep learning
mainly by improving the edge detection stage in the algorithmic pipeline. This
is in contrast to semantic and instance segmentation, where DNNs are applied
directly to generate pixel-wise segment representations. We propose a new
method for learning a pixel-wise representation that reflects segment
relatedness. This representation is combined with an edge map to yield a new
segmentation algorithm. We show that the representations themselves achieve
state-of-the-art segment similarity scores. Moreover, the proposed combined
segmentation algorithm provides results that are either state of the art or
improve upon it, for most quality measures.
