Semantic Line Combination Detector
This addresses the problem of improving semantic line detection for computer vision applications, though it appears incremental as it builds on existing semantic line detectors.
The paper tackles the problem of finding optimal combinations of semantic lines in images by proposing a novel algorithm that processes all lines in each combination simultaneously to assess overall harmony. Experimental results show SLCD outperforms existing semantic line detectors on various datasets and can be effectively applied to three vision tasks: vanishing point detection, symmetry axis detection, and composition-based image retrieval.
A novel algorithm, called semantic line combination detector (SLCD), to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. First, we generate various line combinations from reliable lines. Second, we estimate the score of each line combination and determine the best one. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets. Moreover, it is shown that SLCD can be applied effectively to three vision tasks of vanishing point detection, symmetry axis detection, and composition-based image retrieval. Our codes are available at https://github.com/Jinwon-Ko/SLCD.