Linear perspective is widely used in landscape photography to create the impression of depth on a 2D photo. Automated understanding of the use of linear perspective in landscape photography has a number of real-world applications, including aesthetics assessment, image retrieval, and on-site feedback for photo composition. We address this problem by detecting vanishing points and the associated line structures in photos. However, natural landscape scenes pose great technical challenges because there are often inadequate number of strong edges converging to the vanishing points. To overcome this difficulty, we propose a novel vanishing point detection method that exploits global structures in the scene via contour detection. We show that our method significantly outperforms state-of-the-art methods on a public ground truth landscape image dataset that we have created. Based on the detection results, we further demonstrate how our approach to linear perspective understanding can be used to provide on-site guidance to amateur photographers on their work through a novel viewpoint-specific image retrieval system.
TMM paper > Vanishing_Point_Detection_Landscape
Dataset (Landscape photos from AVA and Flickr)