Interval type-2 possibilistic fuzzy clustering noisy image segmentation algorithm with adaptive spatial constraints and local feature weighting \& clustering weighting
From MaRDI portal
Publication:6114038
DOI10.1016/j.ijar.2023.02.013OpenAlexW4323306087MaRDI QIDQ6114038
Xiaopeng Wang, Shengyang Zhu, Tongyi Wei, Jiaxin Wu
Publication date: 11 July 2023
Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ijar.2023.02.013
interval type-2 fuzzy setslocal feature weightingadaptive spatial constraintsclustering weightingnoisy image segmentationpossibilistic fuzzy C-means
Cites Work
- Fast and robust fuzzy \(c\)-means clustering algorithms incorporating local information for image segmentation
- Generalised kernel weighted fuzzy c-means clustering algorithm with local information
- Alternative c-means clustering algorithms
- Evidential prototype-based clustering based on transfer learning
- A novel interval-valued data driven type-2 possibilistic local information c-means clustering for land cover classification
- A three-way clustering approach using image enhancement operations
- A novel kernelized total Bregman divergence-based fuzzy clustering with local information for image segmentation
- Upper and lower values for the level of fuzziness in FCM
- Further improvements in feature-weighted fuzzy C-means
- Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
- A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
- 10.1162/153244303321897735
- A Robust Fuzzy Local Information C-Means Clustering Algorithm
- Counting People With Low-Level Features and Bayesian Regression
- Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation
This page was built for publication: Interval type-2 possibilistic fuzzy clustering noisy image segmentation algorithm with adaptive spatial constraints and local feature weighting \& clustering weighting
