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Summary: A Framework for Content-Adaptive Photo Manipulation Macros:
Application to Face, Landscape, and Global Manipulations
FLORAINE BERTHOUZOZ
University of California, Berkeley
and
WILMOT LI and MIRA DONTCHEVA
Adobe Systems
and
MANEESH AGRAWALA
University of California, Berkeley
We present a framework for generating content-adaptive macros that can
transfer complex photo manipulations to new target images. We demon-
strate applications of our framework to face, landscape and global manipu-
lations. To create a content-adaptive macro, we make use of multiple train-
ing demonstrations. Specifically, we use automated image labeling and ma-
chine learning techniques to learn the dependencies between image features
and the parameters of each selection, brush stroke and image processing
operation in the macro. Although our approach is limited to learning ma-
nipulations where there is a direct dependency between image features and
operation parameters, we show that our framework is able to learn a large
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