Detecting Dominant Vanishing Points in Natural Scenes with Application
to Composition-Sensitive Image Retrieval
release_dg2ejzcs3fhornwbg2z5qycpje
by
Zihan Zhou, Farshid Farhat, James Z. Wang
2016
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 aesthetics 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.
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