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An Iterative Spanning Forest Framework for Superpixel Segmentation

delete2019-07-01
delete44
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OA
AI
J
John E. Vargas-Muñoz
A
Ananda S. Chowdhury
A
Alexandre, Eduardo B.
G
Galvao, Felipe L.
P
Paulo A. V. Miranda
A
Alexandre X. Falcão *
DOI:10.1109/TIP.2019.2897941delete
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摘要

摘要

En 中文
Superpixel segmentation has emerged as an important research problem in the areas of image processing and computer vision. In this paper, we propose a framework, namely Iterative Spanning Forest (ISF), in which improved sets of connected superpixels (supervoxels in 3D) can be generated by a sequence of image foresting transforms. In this framework, one can choose the most suitable combination of ISF components for a given application-i.e., 1) a seed sampling strategy; 2) a connectivity function; 3) an adjacency relation; and 4) a seed pixel recomputation procedure. The superpixels in ISF structurally correspond to spanning trees rooted at those seeds. We present five ISF-based methods to illustrate different choices for those components. These methods are compared with a number of state-of-the-art approaches with respect to effectiveness and efficiency. Experiments are carried out on several datasets containing 2D and 3D objects with distinct texture and shape properties, including a high-level application, named sky image segmentation. The theoretical properties of ISF are demonstrated in the supplementary material and the results show ISF-based methods rank consistently among the best for all datasets.
Keyword:
Image foresting transform
spanning forests
mixed seed sampling
connectivity function
superpixel/supervoxel segmentation
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期刊

IEEE Transactions on Image Processing 封面图
IEEE Transactions on Image Processing
IF:
13.7
论文数:
1.0W
被引数:
8.4W

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Jadavpur University
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universidade estadual de campinas
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被引数: 19
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universidade de sao paulo
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10.6W
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被引数: 93
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