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Tensor field Graph-Cut for Image Segmentation: A Non-convex Perspective

Zhu, Hu; Zhang, Jieke; Xu, Guoxia; Deng, Lizhen
Peer reviewed, Journal article
Accepted version
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URI
https://hdl.handle.net/11250/2685154
Date
2020
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  • Institutt for datateknologi og informatikk [3866]
  • Publikasjoner fra CRIStin - NTNU [20804]
Original version
10.1109/TCSVT.2020.2995866
Abstract
Image segmentation is a key component of image analysis, which refers to the process of partitioning the image into multiple segments. Graph cut is widely used in image segmentation by constructing a graph that the minimal cut of this graph would lead to partition the corresponding pixels of the different objects. In this paper, we reconstruct the graph cut problem as a special non-convex optimization problem instead of the traditional maximum flow problem. We extend this nonconvex problem to the hypergraph method and combine it with a tensor field based on a directional bilateral filter bank to achieve segmentation in grayscale images. Accordingly, an efficient minimization algorithm is proposed to solve this non-convex problem with global convergence. Furthermore, we have selected the data of BSDS300 and BSDS500 as tests. Experimental results and evaluation index tests further demonstrate the superiority of the proposed method.
Publisher
IEEE
Journal
IEEE transactions on circuits and systems for video technology (Print)

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