Abstract
Replacing overexposed or dull skies in outdoor photographs is a desirable photo manipulation. It is often necessary to color correct the foreground after replacement to make it consistent with the new sky. Methods have been proposed to automate the process of sky replacement and color correction. However, many times a color correction is unwanted by the artist or may produce unrealistic results. We propose a data-driven approach to sky-replacement that avoids color correction by finding a diverse set of skies that are consistent in color and natural illumination with the query image foreground. Our database consists of \(\sim \)1200 natural images spanning many outdoor categories. Given a query image, we retrieve the most consistent images from the database according to \(L_2\) similarity in feature space and produce candidate composites. The candidates are re-ranked based on realism and diversity. We used pre-trained CNN features and a rich set of hand-crafted features that encode color statistics, structural layout, and natural illumination statistics, but observed color statistics to be the most effective for this task. We share our findings on feature selection and show qualitative results and a user-study based evaluation to show the effectiveness of the proposed method.
S. Rawat and S. Gairola— Both authors have contributed equally to this work.
Project page: https://cvit.iiit.ac.in/research/projects/cvit-projects/findmeasky.
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Rawat, S., Gairola, S., Shah, R., Narayanan, P.J. (2018). Find Me a Sky: A Data-Driven Method for Color-Consistent Sky Search and Replacement. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_18
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DOI: https://doi.org/10.1007/978-3-319-73603-7_18
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