Robust semi-supervised spatial picture fuzzy clustering with local membership and KL-divergence for image segmentation

Aiming at existing symmetric regularized picture fuzzy clustering with weak robustness, and it is difficult to meet the need for image segmentation in the presence of high noise. Hence, a robust dynamic semi-supervised symmetric regularized picture fuzzy clustering with KL-divergence and spatial inf...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of machine learning and cybernetics Ročník 13; číslo 4; s. 963 - 987
Hlavní autoři: Wu, Chengmao, Zhang, Jiajia
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2022
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Classification
Clustering
Complex Systems
Computational Intelligence
Control
Engineering
Entropy
Euclidean geometry
Fuzzy sets
Image segmentation
Iterative algorithms
Iterative methods
Maximum entropy
Mechatronics
Neighborhoods
Optimization models
Original Article
Pattern Recognition
Pixels
Robotics
Robustness (mathematics)
Set theory
Spatial data
Systems Biology
Vision systems
KL-divergence
Image segmentation
Semi-supervised method
Local membership
Picture fuzzy clustering
Spatial neighborhood information
ISSN:1868-8071, 1868-808X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Aiming at existing symmetric regularized picture fuzzy clustering with weak robustness, and it is difficult to meet the need for image segmentation in the presence of high noise. Hence, a robust dynamic semi-supervised symmetric regularized picture fuzzy clustering with KL-divergence and spatial information constraints is presented in this paper. Firstly, a weighted squared Euclidean distance from current pixel value, its neighborhood mean and median to clustering center is firstly proposed, and it is embedded into the objective function of symmetric regularized picture fuzzy clustering to obtain spatial picture fuzzy clustering. Secondly, the idea of maximum entropy fuzzy clustering is introduced into picture fuzzy clustering, and an entropy-based picture fuzzy clustering with clear physical meaning is constructed to avoid the problem of selecting weighted factors. Subsequently, the prior information of the current pixel is obtained by means of weighted local membership of neighborhood pixels, and it is embedded into the objective function of maximum entropy picture fuzzy clustering with multiple complementary spatial information constraints through KL-divergence, a robust dynamic semi-supervised picture fuzzy clustering optimization model and its iterative algorithm are given. In the end, this proposed algorithm is strictly proved to be convergent by Zangwill theorem. The experiments on various images and standard datasets illustrate how our proposed algorithm works. This proposed algorithm has excellent segmentation performance and anti-noise robustness, and outperforms eight state-of-the-art fuzzy or picture fuzzy clustering-related algorithms in the presence of high noise.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-021-01429-y