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Journal of Classification

Erschienen in:

09.08.2023 | Original Research

A Robust Contextual Fuzzy C-Means Clustering Algorithm for Noisy Image Segmentation

verfasst von: Karim Kalti, Asma Touil

Erschienen in: Journal of Classification | Ausgabe 3/2023

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Abstract

In this paper, we address the problem of the fuzzy c-means (FCM) algorithm sensitivity to noise when clustering image pixels. We propose in this regard an improved FCM algorithm that incorporates contextual information at the membership degrees updating stage. For that aim, we introduce two novel parameters: the contextual similarity degree and the intrinsic similarity degree which are used to estimate each pixel’s nature (normal or noisy), according respectively to its context and to its specific features. Based on this estimation, we propose a modified membership degrees updating strategy that proceeds by adaptively reinforcing the assignment of a pixel to its context’s cluster when this pixel is detected as noisy. Experiments performed on synthetic and real-world images proved that our approach achieves competitive performance compared to state-of-the-art FCM-based methods.

Vorheriger Artikel Do Prior Information on Performance of Individual Classifiers for Fusion of Probabilistic Classifier Outputs Matter?

Nächster Artikel Expanding the Class of Global Objective Functions for Dissimilarity-Based Hierarchical Clustering

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Titel: A Robust Contextual Fuzzy C-Means Clustering Algorithm for Noisy Image Segmentation
verfasst von: Karim Kalti
Asma Touil
Publikationsdatum: 09.08.2023
Verlag: Springer US
Erschienen in: Journal of Classification / Ausgabe 3/2023
Print ISSN: 0176-4268
Elektronische ISSN: 1432-1343
DOI: https://doi.org/10.1007/s00357-023-09443-1

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