  It is well accepted that image segmentation can benefit from utilizing
multilevel cues. The paper focuses on utilizing the FCNN-based dense semantic
predictions in the bottom-up image segmentation, arguing to take semantic cues
into account from the very beginning. By this we can avoid merging regions of
similar appearance but distinct semantic categories as possible. The semantic
inefficiency problem is handled. We also propose a straightforward way to use
the contour cues to suppress the noise in multilevel cues, thus to improve the
segmentation robustness. The evaluation on the BSDS500 shows that we obtain the
competitive region and boundary performance. Furthermore, since all individual
regions can be assigned with appropriate semantic labels during the
computation, we are capable of extracting the adjusted semantic segmentations.
The experiment on Pascal VOC 2012 shows our improvement to the original
semantic segmentations which derives directly from the dense predictions.
