Image Appearance Exploration by Model-Based Navigation
by Daniel Cohen-Or, Ariel Shamir and Lior Shapira.
Second best paper award!
Abstract
Changing the appearance of an image can be a complex and non-intuitive task.
Many times the target image colors and look are only known vaguely and many
trials are needed to reach the desired results. Moreover, the effect of a
specific change on an image is difficult to envision, since one must take into
account spatial image considerations along with the color constraints. Tools
provided today by image processing applications can become highly technical and
non-intuitive including various gauges and knobs.
In this paper we introduce a method for changing image appearance by
navigation, focusing on recoloring images. The user visually navigates a high
dimensional space of possible color manipulations of an image. He can either
explore in it for inspiration or refine his choices by navigating into sub
regions of this space to a specific goal. This navigation is enabled by
modeling the chroma channels of an image's colors using a Gaussian Mixture
Model (GMM). The Gaussians model both color and spatial image coordinates, and
provide a high dimensional parameterization space of a rich variety of color
manipulations. The user's actions are translated into transformations of the
parameters of the model, which recolor the image. This approach provides both
inspiration and intuitive navigation in the complex space of image color
manipulations.
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