Effective fuzzy clustering algorithm with Bayesian model and mean template for image segmentation

Zhang, Hui; Wu, Q. M. Jonathan; Zheng, Yuhui; Nguyen, Thanh Minh; Wang, Dingcheng

doi:10.1049/iet-ipr.2013.0178

Cited by 22 publications

(30 citation statements)

References 29 publications

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“…The main drawback of the HMRF models is that they are computationally expensive to implement. In order to reduce the computational time as well as enhance the relationships between neighboring pixels, Zhang et al [19] utilized the mean templates of distance function and membership function to improve the segmentation results.…”

Section: Incorporating Adaptive Local Information Into Fuzzy Clusterimentioning

confidence: 99%

“…It is capable of accurately distinguishing pixels in homogeneous regions from those in heterogeneous ones and is also computationally very efficient. In addition, a variable strength factor is used in the prior probability function instead of a fixed one [19] to make the spatial information more accurate. Similar to HMRF-FCM, the negative log-posterior is utilized to form the dissimilarity function, which can further improve the ability to identify the label for each pixel.…”

Section: Incorporating Adaptive Local Information Into Fuzzy Clusterimentioning

confidence: 99%

“…For the sake of computational efficiency, involving more detail local information and reducing the influences of noise, recently some researchers [19], [20] began to define the prior probability function in a fuzzy logical way. In [19], Hui Zhang et al use the mean template on membership function for calculating the prior probability:…”

Section: B Motivation Of Introducing Region-level Information Into Tmentioning

confidence: 99%

“…In [19], Hui Zhang et al use the mean template on membership function for calculating the prior probability:…”

Section: B Motivation Of Introducing Region-level Information Into Tmentioning

confidence: 99%

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Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation

Zhang

Wang

2015

IEEE Trans. on Image Process.

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Fuzzy c-means (FCM) clustering with spatial constraints has attracted great attention in the field of image segmentation. However, most of the popular techniques fail to resolve misclassification problems due to the inaccuracy of their spatial models. This paper presents a new unsupervised FCM-based image segmentation method by paying closer attention to the selection of local information. In this method, region-level local information is incorporated into the fuzzy clustering procedure to adaptively control the range and strength of interactive pixels. First, a novel dissimilarity function is established by combining region-based and pixel-based distance functions together, in order to enhance the relationship between pixels which have similar local characteristics. Second, a novel prior probability function is developed by integrating the differences between neighboring regions into the mean template of the fuzzy membership function, which adaptively selects local spatial constraints by a tradeoff weight depending upon whether a pixel belongs to a homogeneous region or not. Through incorporating region-based information into the spatial constraints, the proposed method strengthens the interactions between pixels within the same region and prevents over smoothing across region boundaries. Experimental results over synthetic noise images, natural color images, and synthetic aperture radar images show that the proposed method achieves more accurate segmentation results, compared with five state-of-the-art image segmentation methods.

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Section: Incorporating Adaptive Local Information Into Fuzzy Clusterimentioning

confidence: 99%

Section: Incorporating Adaptive Local Information Into Fuzzy Clusterimentioning

confidence: 99%

Section: B Motivation Of Introducing Region-level Information Into Tmentioning

confidence: 99%

“…In [19], Hui Zhang et al use the mean template on membership function for calculating the prior probability:…”

Section: B Motivation Of Introducing Region-level Information Into Tmentioning

confidence: 99%

See 2 more Smart Citations

Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation

Zhang

Wang

2015

IEEE Trans. on Image Process.

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“…A variety of improved approaches were proposed to incorporate the spatial information into the original FCM, such as fuzzy local information C-Means (FLICM) [3], Enhanced Fuzzy C-Means Clustering (EnFCM) [4], HMRF-FCM [5], Zhang's method [6], and our proposed methods [7][8][9]. All of them tried to improve the traditional FCM by introducing spatial information in different ways and achieved some excellent results in their intended realms.…”

Section: Introductionmentioning

confidence: 99%

A Novel Histogram-Based Fuzzy Clustering Method for Multispectral Image Segmentation

Li¹,

Zhou²,

Lv³

2017

JEIS

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Fuzzy C-Means (FCM) clustering has been widely used in remote sensing and computer vision. However, when dealing with multispectral images, the conventional FCM regards spectral responses of all bands on each pixel as a feature vector and conducts image clustering by searching cluster centers in a multi-dimensional space. It is rather time-consuming due to the fact that it has to visit each pixel many rounds during the iteration procedure. Besides, it is sensitive to noise, which mainly results from its ignorance spatial information. In order to overcome these problems, a novel histogram-based fuzzy clustering method is presented in this paper. The proposed method clusters each band independently and fuses the results to form the final segmentation map. On each band, a spatial-spectral image is computed previously, and then the histogram of this image is exploited to find the initial clusters, which is followed by a clustering procedure directly performed on the histogram instead of image pixels. The experimental results over remote sensing images show that the proposed method can achieve more accurate results but uses less time.

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Superpixel‐Based Fast Image Segmentation

2022

Image Segmentation

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Effective fuzzy clustering algorithm with Bayesian model and mean template for image segmentation

Cited by 22 publications

References 29 publications

Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation

Incorporating Adaptive Local Information Into Fuzzy Clustering for Image Segmentation

A Novel Histogram-Based Fuzzy Clustering Method for Multispectral Image Segmentation

Superpixel‐Based Fast Image Segmentation

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