Abstract
Subspace clustering has been widely used in image segmentation. These methods usually use superpixel segmentation to pre-segment image, while the superpixel segmentation method always divides image into superpixel blocks of similar shape and size, which results in poor segmentation and is time-consuming. In addition, the existing image segmentation methods based on subspace clustering don’t consider processing nonlinear structure data and complex noise. In order to solve the above problems, this paper proposes an image segmentation method (AMR_WT_NLMSC) based on non-convex low-rank multi-kernel clustering, which uses the adaptive morphological reconstruction seed segmentation (AMR_WT) for pre-segmentation, and designs non-convex low-rank multi-kernel subspace clustering (NLMSC) achieves the final segmentation. Experiments on real image datasets show that AMR_WT _NLMSC method has the more accurate segmentation effect.
This work has been supported in part by the National Natural Science Foundation of China under Grant 62102331, the National Natural Science Foundation of China under Grant 61772272, the Sichuan Province Science and Technology Support Program under Grant Nos. 2020YJ0432, 2020YFS0360, 18YYJC1688 and 18ZB0611, and the Postgraduate Innovation Fund Project by Southwest University of Science and Technology under Grant 20ycx0032.
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Xue, X., Wang, X., Zhang, X., Wang, J., Liu, Z. (2021). Image Segmentation Based on Non-convex Low Rank Multiple Kernel Clustering. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_36
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