Semantic lines characterize the layout of an image. Despite their importance in image analysis and scene understanding, there is no reliable research for semantic line detection. In this paper, we propose a semantic line detector using a convolutional neural network with multi-task learning, by regarding the line detection as a combination of classification and regression tasks. We use convolution and max-pooling layers to obtain multi-scale feature maps for an input image. Then, we develop the line pooling layer to extract a feature vector for each candidate line from the feature maps. Next, we feed the feature vector into the parallel classification and regression layers. The classification layer decides whether the line candidate is semantic or not. In case of a semantic line, the regression layer determines the offset for refining the line location. Experimental results show that the proposed detector extracts semantic lines accurately and reliably. Moreover, we demonstrate that the proposed detector can be used successfully in three applications: horizon estimation, composition enhancement, and image simplification.
Jun-Tae Lee, Han-Ul Kim, Chul Lee, and Chang-Su Kim, "Semantic Line Detection and Its Applications," in Proc. International Conference on Computer Vision (ICCV), Venice, Italy, pp. 3229-3237, Oct. 2017. [code]
Jun-Tae Lee, Han-Ul Kim, Chul Lee, and Chang-Su Kim, "SLNet: A Semantic Line Detector with Multi-Task and Multi-Scale Learning," submitted to IEEE Access.
You may download the SEL dataset from here.