Journal ArticleDOI
A Robust Fuzzy Local Information C-Means Clustering Algorithm
TLDR
A variation of fuzzy c-means (FCM) algorithm that provides image clustering that incorporates the local spatial information and gray level information in a novel fuzzy way, called fuzzy local information C-Means (FLICM).Abstract:
This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of FLICM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation. Furthermore, the proposed algorithm is fully free of the empirically adjusted parameters (a, ?g, ?s, etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature. Experiments performed on synthetic and real-world images show that FLICM algorithm is effective and efficient, providing robustness to noisy images.read more
Citations
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Journal ArticleDOI
Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation
TL;DR: An improved fuzzy C-means (FCM) algorithm for image segmentation is presented by introducing a tradeoff weighted fuzzy factor and a kernel metric and results show that the new algorithm is effective and efficient, and is relatively independent of this type of noise.
Journal ArticleDOI
Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering
TL;DR: An unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm that exhibited lower error than its preexistences.
Journal ArticleDOI
Image segmentation by generalized hierarchical fuzzy C-means algorithm
TL;DR: This paper introduces a new generalized hierarchical FCM (GHFCM), which is more robust to image noise with the spatial constraints: the generalized mean, and introduces a more flexibility function which considers the distance function itself as a sub-FCM.
Journal ArticleDOI
Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering
TL;DR: An improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper and demonstrates that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation.
Journal ArticleDOI
Fuzzy Clustering With a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images
TL;DR: Theoretical analysis and experimental results on real SAR datasets show that the proposed approach can detect the real changes as well as mitigate the effect of speckle noises and is computationally simple in all the steps involved.
References
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Journal ArticleDOI
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
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