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. 20, no. 2, pp. 496-508, 2018.
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Last Modified:
July 28, 2017
© 2017