Color Photo Makeover via Crowd Sourcing and Recoloring

Cheng, Wengang; Jiang, Ruru; Chen, Chang Wen

doi:10.1145/2733373.2806370

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Kumar et al [10] proposed to create correspondences between superpixels by fast cascade feature matching, and then refine the transfer results by a voting approach. Cheng et al [3] proposed a superpixel-based recoloring scheme based on a soft matching embedded with color statistics, texture char-acteristics and spatial constraints to generate new recolored images. There are also some works that aim to conduct favorite exemplars recommendation based on visual information [3] or patch aggregation [27].…”

Section: Example-based Style Transfermentioning

confidence: 99%

“…Cheng et al [3] proposed a superpixel-based recoloring scheme based on a soft matching embedded with color statistics, texture char-acteristics and spatial constraints to generate new recolored images. There are also some works that aim to conduct favorite exemplars recommendation based on visual information [3] or patch aggregation [27]. Compared with previous methods, our method aims to address the general style transfer of photos instead of a certain style element, such as only color, or some artistic styles.…”

Section: Example-based Style Transfermentioning

confidence: 99%

“…Considering that the pixels in a superpixel share similar features, for similarity, hierarchical features of a superpixel are defined as the mean vector of hierarchical features of pixels within it. And the affinities between superpixel bipartite graph are calculated based on the superpixel hierarchical feature, in the same way as (3). Then, SuperBIG solves the bipartite graph matching by the Hungarian algorithm [2], obtaining final superpixel correspondences F c f and F r f .…”

Section: Superpixel Bipartite Graph Matchingmentioning

confidence: 99%

See 2 more Smart Citations

Photo Stylistic Brush: Robust Style Transfer via Superpixel-Based Bipartite Graph

Liu

Yang

Sun

et al. 2018

IEEE Trans. Multimedia

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With the rapid development of social network and multimedia technology, customized image and video stylization has been widely used for various social-media applications. In this paper, we explore the problem of exemplar-based photo style transfer, which provides a flexible and convenient way to invoke fantastic visual impression. Rather than investigating some fixed artistic patterns to represent certain styles as was done in some previous works, our work emphasizes styles related to a series of visual effects in the photograph, e.g. color, tone, and contrast. We propose a photo stylistic brush, an automatic robust style transfer approach based on Superpixel-based BIpartite Graph (SuperBIG). A twostep bipartite graph algorithm with different granularity levels is employed to aggregate pixels into superpixels and find their correspondences. In the first step, with the extracted hierarchical features, a bipartite graph is constructed to describe the content similarity for pixel partition to produce superpixels. In the second step, superpixels in the input/reference image are rematched to form a new superpixelbased bipartite graph, and superpixel-level correspondences are generated by a bipartite matching. Finally, the refined correspondence guides SuperBIG to perform the transformation in a decorrelated color space. Extensive experimental results demonstrate the effectiveness and robustness of the proposed method for transferring various styles of exemplar images, even for some challenging cases, such as night images.

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