Please use this identifier to cite or link to this item: https://doi.org/10.1109/TMM.2013.2241042
Title: Learning to photograph: A compositional perspective
Authors: Ni, B.
Xu, M.
Cheng, B.
Wang, M.
Yan, S. 
Tian, Q.
Keywords: Generative model
maximum a posteriori
photo composition
spatial context
view recommendation
Issue Date: 2013
Source: Ni, B.,Xu, M.,Cheng, B.,Wang, M.,Yan, S.,Tian, Q. (2013). Learning to photograph: A compositional perspective. IEEE Transactions on Multimedia 15 (5) : 1138-1151. ScholarBank@NUS Repository. https://doi.org/10.1109/TMM.2013.2241042
Abstract: In this paper, we present an intelligent photography system which can recommend the most user-favored view rectangle for arbitrary camera input, from a photographic compositional perspective. Automating this process is difficult, due to the subjectivity of human's aesthetics judgement and large variations of image contents, where heuristic compositional rules lack generality. Motivated by the recent prevalence of photo-sharing websites, e.g., Flickr.com, we develop a learning-based framework which discovers the underlying aesthetic photographic compositional structures from a large set of user-favored online sharing photographs and utilizes the implicitly shared knowledge among the professional photographers for aesthetically optimal view recommendation. In particular, we propose an Omni-Range Context method which explicitly encodes the spatial and geometric distributions of various visual elements in the photograph as well as cooccurrence characteristics of visual element pairs by using generative mixture models. Searching the optimal view rectangle is then formulated as maximum a posterior by imposing the trained prior distributions along with additional photographic constraints. The proposed system has the potential to operate in near real-time. Comprehensive user studies well demonstrate the effectiveness of the proposed framework for aesthetically optimal view recommendation. © 1999-2012 IEEE.
Source Title: IEEE Transactions on Multimedia
URI: http://scholarbank.nus.edu.sg/handle/10635/56479
ISSN: 15209210
DOI: 10.1109/TMM.2013.2241042
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