Multi-scale Information Assembly for Image Matting

dc.contributor.authorQiao, Yuen_US
dc.contributor.authorLiu, Yuhaoen_US
dc.contributor.authorZhu, Qiangen_US
dc.contributor.authorYang, Xinen_US
dc.contributor.authorWang, Yuxinen_US
dc.contributor.authorZhang, Qiangen_US
dc.contributor.authorWei, Xiaopengen_US
dc.contributor.editorEisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lueen_US
dc.description.abstractImage matting is a long-standing problem in computer graphics and vision, mostly identified as the accurate estimation of the foreground in input images.We argue that the foreground objects can be represented by different-level information, including the central bodies, large-grained boundaries, refined details, etc. Based on this observation, in this paper, we propose a multi-scale information assembly framework (MSIA-matte) to pull out high-quality alpha mattes from single RGB images. Technically speaking, given an input image, we extract advanced semantics as our subject content and retain initial CNN features to encode different-level foreground expression, then combine them by our well-designed information assembly strategy. Extensive experiments can prove the effectiveness of the proposed MSIA-matte, and we can achieve state-of-the-art performance compared to most existing matting networks.en_US
dc.description.sectionheadersImage Manipulation
dc.description.seriesinformationComputer Graphics Forum
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectImage segmentation
dc.subjectImage representations
dc.titleMulti-scale Information Assembly for Image Mattingen_US