Improved clustering algorithms for image segmentation based on non-local information and back projection

DOI10.1016/j.ins.2020.10.039zbMath1483.68478OpenAlexW3094167210MaRDI QIDQ2123512

Caiming Zhang, Feng Zhao, Zhongjun Hou, Yu-Juan Sun, Hui Liu, Xiaofeng Zhang

Publication date: 14 April 2022

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.10.039

zbMATH Keywords

self-similarityfuzzy clusteringimage segmentationback projectionnon-local information

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Image analysis in multivariate analysis (62H35) Computing methodologies for image processing (68U10) Multivariate analysis and fuzziness (62H86)

Related Items (5)

Learning deep discriminative representations with pseudo supervision for image clustering ⋮ ROBP a robust border-peeling clustering using Cauchy kernel ⋮ A novel method for optimizing spectral rotation embedding \(K\)-means with coordinate descent ⋮ Improved interval type-2 fuzzy K-means clustering based on adaptive iterative center with new defuzzification method ⋮ On efficient model selection for sparse hard and fuzzy center-based clustering algorithms

Uses Software

• SegNet

Cites Work

• Fast and robust fuzzy \(c\)-means clustering algorithms incorporating local information for image segmentation
• On fuzzy cluster validity indices
• A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
• A Robust Fuzzy Local Information C-Means Clustering Algorithm
• Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation
• A Review of Image Denoising Algorithms, with a New One

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