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Improved clustering algorithms for image segmentation based on non-local information and back projection
- Source :
- Information Sciences. 550:129-144
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- 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.
- Subjects :
- Information Systems and Management
Computer science
business.industry
05 social sciences
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
050301 education
Pattern recognition
02 engineering and technology
Image segmentation
Non local
Computer Science Applications
Theoretical Computer Science
Artificial Intelligence
Control and Systems Engineering
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Segmentation
Artificial intelligence
business
Cluster analysis
0503 education
Back projection
Software
Subjects
Details
- ISSN :
- 00200255
- Volume :
- 550
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi...........3a492b3ae3ef811583abc8c654195415