Discovering Triangles in Portraits for Supporting Photographic Creation

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

Abstract:

Incorporating the concept of triangles in photos is an effective composition technique used by professional photographers for making pictures more interesting or dynamic. Information on the locations of the embedded triangles is valuable for comparing the composition of portrait photos, which can be further leveraged by a retrieval system or used by the photographers. This paper presents a system to automatically detect embedded triangles in portrait photos. The problem is challenging because the triangles used in portraits are often not clearly defined by straight lines. The system first extracts a set of filtered line segments as candidate triangle sides, and then utilizes a modified RANSAC algorithm to fit triangles onto the set of line segments. We propose two metrics, Continuity Ratio and Total Ratio, to evaluate the fitted triangles; those with high fitting scores are taken as detected triangles. Experimental results have demonstrated high accuracy in locating preeminent triangles in portraits without dependence on the camera or lens parameters. To demonstrate the benefits of our method to digital photography, we have developed two novel applications that aim to help users compose high-quality photos. In the first application, we develop a human position and pose recommendation system by retrieving and presenting compositionally similar photos taken by competent photographers. The second application is a novel sketch-based triangle retrieval system which searches for photos containing a specific triangular configuration. User studies have been conducted to validate the effectiveness of these approaches.


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Citation: Siqiong He, Zihan Zhou, Farshid Farhat and James Z. Wang, ``Discovering Triangles in Portraits for Supporting Photographic Creation,'' IEEE Transactions on Multimedia, vol. 19, no. , 13 pages, 2017.

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Last Modified: July 28, 2017
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