  A novel algorithm to detect an optimal set of semantic lines is proposed in
this work. We develop two networks: selection network (S-Net) and harmonization
network (H-Net). First, S-Net computes the probabilities and offsets of line
candidates. Second, we filter out irrelevant lines through a
selection-and-removal process. Third, we construct a complete graph, whose edge
weights are computed by H-Net. Finally, we determine a maximal weight clique
representing an optimal set of semantic lines. Moreover, to assess the overall
harmony of detected lines, we propose a novel metric, called HIoU. Experimental
results demonstrate that the proposed algorithm can detect harmonious semantic
lines effectively and efficiently. Our codes are available at
https://github.com/dongkwonjin/Semantic-Line-MWCS.
