Abstract
Color transfer methods can alter color appearance in the input image by borrowing color statistics from the reference image. In this paper we present a novel color transfer method in which we consider both input and reference images as three-dimensional sets of data samples, where each color based component can be represented as a 3D cloud of data points. Our goal is to fit position, orientation and scale of color-component clouds from reference to input image by finding proper geometric transformation. Besides global processing approach we also present local color transfer method by applying our proposed algorithm to color segmented parts of images. We use pixel clustering for image segmentation to find groups of dominant colors pixels in each of input and reference images. Experimental results and comparisons with other methods confirm the validity and usefulness of presented method.
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