Learning a Discriminative Model for the Perception of Realism in Composite Images



What makes an image appear realistic? In this work, we are answering this question from a data-driven perspective by learning the perception of visual realism directly from large amounts of data. In particular, we train a Convolutional Neural Network (CNN) model that distinguishes natural photographs from automatically generated composite images. The model learns to predict visual realism of a scene in terms of color, lighting and texture compatibility, without any human annotations pertaining to it. Our model outperforms previous works that rely on hand-crafted heuristics, for the task of classifying realistic vs. unrealistic photos. Furthermore, we apply our learned model to compute optimal parameters of a composition method, to maximize the visual realism score predicted by our CNN model. We demonstrate its advantage against existing methods via a human perception study.

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ICCV 2015 paper, 2.6MB


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ICCV poster, 1.8 MB


Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman and Alexei A. Efros. "Learning a Discriminative Model for the Perception of Realism in Composite Images", in IEEE International Conference on Computer Vision (ICCV). 2015. Bibtex



Additional Materials


We thank Jean-François Lalonde and Xue Su for help with running their code.


This research is supported in part by: