Adaptive local data and membership based KL divergence incorporating C-means algorithm for fuzzy image segmentation

Gharieb, R.R.; Gendy, Garas; Abdelfattah, Ahmed; Selim, Hany

doi:10.1016/j.asoc.2017.05.055

Cited by 32 publications

(19 citation statements)

References 17 publications

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“…In [18], an approach to incorporating local spatial membership information into HCM algorithm has been presented. By adding Kullback-Leibler (KL) divergence between the membership function of an entity and the locally-smoothed membership in the immediate spatial neighborhood, the modified objective function, called the local membership KL divergencebased FCM (LMKLFCM), is given by [18][19][20][21][22].…”

Section: Hcm Incorporating Local Membership Kl Divergencementioning

confidence: 99%

“…where γ is a weighting parameter experimentally selected to control the fuzziness induced by the second term in (19), u in ¼ 1 À u in is the complement of the membership function u in , π in and π in are the spatial local or moving averages of membership u in and the complement membership u in , functions respectively. These local membership and membership complement averages are computed by [18][19][20][21][22].…”

Section: Hcm Incorporating Local Membership Kl Divergencementioning

confidence: 99%

“…where x n is the noise-free pixel in the synthetic image and 1 and 0 represent True and False, respectively. After the segmentation is done, the estimated labels are also formulated as logical matrices generated by [20].…”

Section: Clustering Validitymentioning

confidence: 99%

“…In this chapter, HCM clustering algorithm is modified by incorporating local spatial data and Kullback-Leibler (KL) membership divergence [18][19][20][21][22]. The local data information is incorporated via an additional weighted HCM function in which the smoothed image data is used for the distance computation.…”

Section: Introductionmentioning

confidence: 99%

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Incorporating Local Data and KL Membership Divergence into Hard C-Means Clustering for Fuzzy and Noise-Robust Data Segmentation

Gharieb¹

2018

Recent Applications in Data Clustering

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Hard C-means (HCM) and fuzzy C-means (FCM) algorithms are among the most popular ones for data clustering including image data. The HCM algorithm offers each data entity with a cluster membership of 0 or 1. This implies that the entity will be assigned to only one cluster. On the contrary, the FCM algorithm provides an entity with a membership value between 0 and 1, which means that the entity may belong to all clusters but with different membership values. The main disadvantage of both HCM and FCM algorithms is that they cluster an entity based on only its self-features and do not incorporate the influence of the entity's neighborhoods, which makes clustering prone to additive noise. In this chapter, Kullback-Leibler (KL) membership divergence is incorporated into the HCM for image data clustering. This HCM-KL-based clustering algorithm provides twofold advantage. The first one is that it offers a fuzzification approach to the HCM clustering algorithm. The second one is that by incorporating a local spatial membership function into the HCM objective function, additive noise can be tolerated. Also spatial data is incorporated for more noise-robust clustering.

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Section: Hcm Incorporating Local Membership Kl Divergencementioning

confidence: 99%

Section: Hcm Incorporating Local Membership Kl Divergencementioning

confidence: 99%

Section: Clustering Validitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incorporating Local Data and KL Membership Divergence into Hard C-Means Clustering for Fuzzy and Noise-Robust Data Segmentation

Gharieb¹

2018

Recent Applications in Data Clustering

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“…Recently, some comprehensive FCM-related algorithms have been presented [19]- [21], which involve various techniques. For instance, Gharieb et al [19] introduce a FCM framework by using Kullback-Leibler divergence to control the membership distance between a pixel and its neighbors. However, their algorithm is time-consuming and its segmentation effects can be further improved.…”

Section: Introductionmentioning

confidence: 99%

Sparse Regularization-Based Fuzzy C-Means Clustering Incorporating Morphological Grayscale Reconstruction and Wavelet Frames

Wang

Pedrycz

Zhou

et al. 2021

IEEE Trans. Fuzzy Syst.

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Instead of directly utilizing an observed image including some outliers, noise or intensity inhomogeneity, the use of its ideal value (e.g. noise-free image) has a favorable impact on clustering. Hence, the accurate estimation of the residual (e.g. unknown noise) between the observed image and its ideal value is an important task. To do so, we propose an ℓ0 regularization-based Fuzzy C-Means (FCM) algorithm incorporating a morphological reconstruction operation and a tight wavelet frame transform. To achieve a sound trade-off between detail preservation and noise suppression, morphological reconstruction is used to filter an observed image. By combining the observed and filtered images, a weighted sum image is generated. Since a tight wavelet frame system has sparse representations of an image, it is employed to decompose the weighted sum image, thus forming its corresponding feature set. Taking it as data for clustering, we present an improved FCM algorithm by imposing an ℓ0 regularization term on the residual between the feature set and its ideal value, which implies that the favorable estimation of the residual is obtained and the ideal value participates in clustering. Spatial information is also introduced into clustering since it is naturally encountered in image segmentation. Furthermore, it makes the estimation of the residual more reliable. To further enhance the segmentation effects of the improved FCM algorithm, we also employ the morphological reconstruction to smoothen the labels generated by clustering. Finally, based on the prototypes and smoothed labels, the segmented image is reconstructed by using a tight wavelet frame reconstruction operation. Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other algorithms.

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Adaptive Fuzzy Clustering Algorithm with Local Information and Markov Random Field for Image Segmentation

Wen

2018

Neural Information Processing

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Adaptive local data and membership based KL divergence incorporating C-means algorithm for fuzzy image segmentation

Cited by 32 publications

References 17 publications

Incorporating Local Data and KL Membership Divergence into Hard C-Means Clustering for Fuzzy and Noise-Robust Data Segmentation

Incorporating Local Data and KL Membership Divergence into Hard C-Means Clustering for Fuzzy and Noise-Robust Data Segmentation

Sparse Regularization-Based Fuzzy C-Means Clustering Incorporating Morphological Grayscale Reconstruction and Wavelet Frames

Adaptive Fuzzy Clustering Algorithm with Local Information and Markov Random Field for Image Segmentation

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