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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.

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DOI: 10.1111/cgf.14156

Abstract

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
ISSN:0167-7055
Group:Faculty of Media & Communication
ID Code:34964
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:15 Dec 2020 14:19
Last Modified:24 Nov 2021 01:08

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