1 research outputs found
Learning Pixel Representations for Generic Segmentation
Deep learning approaches to generic (non-semantic) segmentation have so far
been indirect and relied on edge detection. This is in contrast to semantic
segmentation, where DNNs are applied directly. We propose an alternative
approach called Deep Generic Segmentation (DGS) and try to follow the path used
for semantic segmentation. Our main contribution is a new method for learning a
pixel-wise representation that reflects segment relatedness. This
representation is combined with a CRF to yield the segmentation algorithm. We
show that we are able to learn meaningful representations that improve
segmentation quality and that the representations themselves achieve
state-of-the-art segment similarity scores. The segmentation results are
competitive and promising