Edge-Sensitive Human Cutout with Hierarchical Granularity and Loopy Matting Guidance.

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ID: 96735
2019
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Ranked #8 of 51 articles by views in ieee transactions on image processing : a publication of the ieee signal processing society

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Abstract
Human parsing and matting play important roles in various applications, such as dress collocation, clothing recommendation, and image editing. In this paper, we propose a lightweight hybrid model that unifies the fully-supervised hierarchical-granularity parsing task and the unsupervised matting one. Our model comprises two parts, the extensible hierarchical semantic segmentation block using CNN and the matting module composed of guided filters. Given a human image, the segmentation block stage-1 first obtains a primitive segmentation map to separate the human and the background. The primitive segmentation is then fed into stage-2 together with the original image to give a rough segmentation of human body. This procedure is repeated in the stage-3 to acquire a refined segmentation. The matting module takes as input the above estimated segmentation maps and produces the matting map, in a fully unsupervised manner. The obtained matting map is then in turn fed back to the CNN in the first block for refining the semantic segmentation results.
Reference Key
ye2019edgesensitiveieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ye, Jingwen;Jing, Yongcheng;Wang, Xinchao;Ou, Kairi;Tao, Dacheng;Song, Mingli;
Journal ieee transactions on image processing : a publication of the ieee signal processing society
Year 2019
DOI
10.1109/TIP.2019.2930146
URL
Keywords
semantics memory association judgments thematics

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