Adaptive Non-local Affinity Graph for Unsupervised Image Segmentation

被引：0
|
作者：
Lv, Xin [1 ]
Su, Zhenming [1 ]
Zhang, Taiyi [1 ]
Cheng, Wenxiang [1 ]
Qi, Xiaoqiong [2 ]
机构：
[1] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Peoples R China
[2] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane, Qld, Australia
来源：
2023 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, ICME | 2023年
关键词：
unsupervised image segmentation; graph construction; spectral segmentation;
D O I：
10.1109/ICME55011.2023.00402
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present an adaptive superpixel-based graph construction method for unsupervised image segmentation. The empirical findings in this study provide a new understanding of applying non-local image patches to image segmentation. We demonstrate that high-level segmentation cues can be revealed by non-local similar patches, and based on it the similarity between superpixels overlapped with these similar patches can be adaptively constructed. The experimental result on the Berkeley Segmentation Database demonstrates the effectiveness and superiority of the proposed algorithm.
引用
收藏
页码：2357 / 2362
页数：6
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