Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval

Zihan Zhou, Farshid Farhat and James Z. Wang
The Pennsylvania State University

Abstract:

Linear perspective is widely used in landscape photography to create the impression of depth on a 2D photo. Automated understanding of linear perspective in landscape photography has several real-world applications, including aes- thetics assessment, image retrieval, and on-site feedback for photo composition, yet adequate automated understanding has been elusive. We address this problem by detecting the dominant vanishing point and the associated line structures in a photo. However, natural landscape scenes pose great technical challenges because often the inadequate number of strong edges converging to the dominant vanishing point is inadequate. 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 provides on-site guidance to amateur photographers on their work through a novel viewpoint-specific image retrieval system.

Full Paper
(PDF, 13MB)

Datasets (dominant vanishing points in 1,316 images from the AVA landscape dataset and 959 images from Flickr)
(ZIP, 453KB)

Citation: Zihan Zhou, Farshid Farhat and James Z. Wang, ``Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval,'' IEEE Transactions on Multimedia, vol. 19, no. 12, pp. 2651-2665, 2017.

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Last Modified: May 9, 2017
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