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

TL;DR

This paper introduces a novel method for detecting dominant vanishing points in natural landscape scenes by leveraging global scene structures, improving accuracy over existing methods, and applying it to enhance image retrieval and photographic guidance.

Contribution

The paper presents a new vanishing point detection technique that exploits global scene contours, addressing challenges in natural landscapes and enabling improved perspective understanding.

Findings

Outperforms state-of-the-art vanishing point detection methods on landscape datasets.

Enables viewpoint-specific image retrieval for photography guidance.

Demonstrates practical application in aesthetics assessment and composition feedback.

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.