Learning a Discriminative Model for the Perception of Realism in Composite Images
2015 IEEE International Conference on Computer Vision (ICCV)(2015)
摘要
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 compositing 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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关键词
discriminative model,realism perception,composite images,convolutional neural network training,CNN model,natural photographs,visual realism prediction,unrealistic photo classification,human perception
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