Journal
INFORMATION SCIENCES
Volume 550, Issue -, Pages 129-144Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.10.039
Keywords
Image segmentation; Fuzzy clustering; Non-local information; Self-similarity; Back projection
Categories
Funding
- NSF of China [61873117, 62072274, U1609218, 61872170, 61773244, 61772253, 61771231, 61903172]
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This paper proposes an improved image segmentation schema and presents two improved clustering algorithms that consider self-similarity and back projection simultaneously to enhance robustness, balancing noise restraining and detail retention in segmentation of complex images.
Accurate image segmentation is a prerequisite to conducting an image analysis task, and the complexity stemming from the semantic diversity plays a pivotal role in image segmentation. Existing algorithms employed different types of information in the process of segmentation to improve the robustness. However, these algorithms were characterized by a tradeoff between noise removal and detail retention; this is because it is difficult to distinguish image artifacts from details. This paper proposes an improved image segmentation schema and presents two improved clustering algorithms, in which self-similarity and back projection are considered simultaneously to enhance the robustness. With the aid of self-similarity, non-local information is fully exploited, while the original information can be retained by back projection. Extensive experiments on various types of images demonstrate that our algorithms can balance noise restraining and detail retention to improve the adaptation of complex images in segmentation. (C) 2020 Elsevier Inc. All rights reserved.
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