Browsing by Author "Brown, Michael S."

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    Image Recoloring Based on Object Color Distributions
    (The Eurographics Association, 2019) Afifi, Mahmoud; Price, Brian; Cohen, Scott; Brown, Michael S.; Cignoni, Paolo and Miguel, Eder
    We present a method to perform automatic image recoloring based on the distribution of colors associated with objects present in an image. For example, when recoloring an image containing a sky object, our method incorporates the observation that objects of class 'sky' have a color distribution with three dominant modes for blue (daytime), yellow/red (dusk/dawn), and dark (nighttime). Our work leverages recent deep-learning methods that can perform reasonably accurate object-level segmentation. By using the images in datasets used to train deep-learning object segmentation methods, we are able to model the color distribution of each object class in the dataset. Given a new input image and its associated semantic segmentation (i.e., object mask), we perform color transfer to map the input image color histogram to a set of target color histograms that were constructed based on the learned color distribution of the objects in the image. We show that our framework is able to produce compelling color variations that are often more interesting and unique than results produced by existing methods.
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    Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Xue, Danna; Corral, Javier Vazquez; Herranz, Luis; Zhang, Yanning; Brown, Michael S.; Chaine, Raphaƫlle; Deng, Zhigang; Kim, Min H.
    Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework.