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Generating High-quality Superpixels in Textured Images.

Zhang, Z., Xu, P., Chang, J., Wang, W., Zhao, C. and Zhang, J. J., 2020. Generating High-quality Superpixels in Textured Images. Computer Graphics Forum, 39 (7), 421 - 431.

Full text available as:

paper1163.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


DOI: 10.1111/cgf.14156


Superpixel segmentation is important for promoting various image processing tasks. However, existing methods still have difficulties in generating high-quality superpixels in textured images, because they cannot separate textures from structures well. Though texture filtering can be adopted for smoothing textures before superpixel segmentation, the filtering would also smooth the object boundaries, and thus weaken the quality of generated superpixels. In this paper, we propose to use the adaptive scale box smoothing instead of the texture filtering to obtain more high-quality texture and boundary information. Based on this, we design a novel distance metric to measure the distance between different pixels, which considers boundary, color and Euclidean distance simultaneously. As a result, our method can achieve high-quality superpixel segmentation in textured images without texture filtering. The experimental results demonstrate the superiority of our method over existing methods, even the learning-based methods. Benefited from using boundaries to guide superpixel segmentation, our method can also suppress noise to generate high-quality superpixels in non-textured images.

Item Type:Article
Group:Faculty of Media & Communication
ID Code:34964
Deposited By: Symplectic RT2
Deposited On:15 Dec 2020 14:19
Last Modified:14 Mar 2022 14:25


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