Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation

Fan, Jianchao; Han, Min; Wang, Jun

doi:10.1016/j.patcog.2009.04.013

Cited by 99 publications

(35 citation statements)

References 39 publications

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“…The proposed ColorRcP clustering algorithm is evaluated here, and its performance has been compared with the following FCM-based segmentation methods: the Hidden Markov random field FCM (HMRFFCM) [9], the histogram thresholding FCM (HTFCM) [10], the fuzzy hyper-prototype clustering (FHCS) [11], the quantized FCM (QFCM_S2) [12], the usual-initialization FCM (UFCM) [13], the color-clustering FCM (CFCM) [14], and the single point iterative weighted FCM (SWFCM) [15]. We also compare our proposal with other image segmentation techniques beyond FCM such as the penalized inverse expectation maximization (PIEM) which extracts the features for each pixel using the Gabor filter, and the classification of pixels in different regions is done by the expectation maximization (EM) algorithm [25] and the segmentation by clustering then labeling (SCLpost) which uses small homogeneous regions to adopt a 3D feature vector obtained through the color space, and then, the clustering is done by a hybrid approach that combines the mean-shift with a semisupervised discriminant analysis algorithm [26].…”

Section: Results and Comparisonsmentioning

confidence: 99%

“…As can be seen in the images, the ColorRcP To demonstrate the performance of the proposed ColorRcP clustering scheme in real applications, a subregion from the remote sensing image 'Zhalong Nature Reserve' (see Figure 6a) is segmented [15]. The proposed algorithm is compared with other approaches designed to segment this kind of images, such as the UFCM, CFCM, and SWFCM algorithms [13][14][15].…”

Section: Results and Comparisonsmentioning

confidence: 99%

“…Dae-Won et al proposed in [14] an algorithm that extracts the most vivid and distinguishable colors referred as the dominant colors, the color points closest to these dominant colors are selected as the initial centroids in the FCM calculations using a fuzzy membership model between a color point and a reference color. Finally, an approach designed to promote an optimal initialization scheme of the FCM algorithm using a single point iterative weighted based upon the prior information was presented in [15].…”

Section: Introductionmentioning

confidence: 99%

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Robust c-prototypes algorithms for color image segmentation

et al. 2013

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In this paper, we present a modified clustering algorithm to segment color images. The proposed technique is based on the robust c-prototypes algorithm with some modifications in its objective function and updating equations. The JK chromatic subspace of the IJK color space is used to segment color images. The algorithm performance is tested on real images with natural artifacts that make the segmentation process difficult. Additionally, a remote sensing image is segmented to demonstrate that the proposed algorithm can be used in real applications. Simulation results indicate that the proposed method is more effective than others proposed in the literature in terms of objective and subjective criteria.

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Section: Results and Comparisonsmentioning

confidence: 99%

Section: Results and Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust c-prototypes algorithms for color image segmentation

et al. 2013

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“…The modified FCM algorithm [7] mentioned above has been successfully applied on remote sensing images as well as synthetic images. In [27], a remote sensing image segmentation procedure that utilizes a single point iterative weighted fuzzy C-Means clustering algorithm is proposed based on the prior information.…”

Section: Introductionmentioning

confidence: 99%

Color Image Segmentation using Fast Fuzzy C-Means Algorithm

Bhoyar

Kakde²

2010

ELCVIA

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This paper proposes modified FCM (Fuzzy C-Means) approach to colour image segmentation using JND (Just Noticeable Difference) histogram. Histogram of the given colour image is computed using JND colour model. This samples the colour space so that just enough number of histogram bins are obtained without compromising the visual image content. The number of histogram bins are further reduced using agglomeration. This agglomerated histogram yields the estimation of number of clusters, cluster seeds and the initial fuzzy partition for FCM algorithm. This is a novell approach to estimate the input parameters for FCM algorithm. The proposed fast FCM(FFCM) algorithm works on histogram bins as data elements instead of individual pixels. This significantly reduces the time complexity of FCM algorithm. To verify the effectiveness of the proposed image segmentation approach, its performance is evaluated on Berkeley Segmentation Database(BSD). Two significant criteria namely PSNR(Peak Signal to Noise Ratio) and PRI (Probabilistic Rand Index) are used to evaluate the performance. Although results show that the proposed algorithm applied to the JND histogram bins converges much faster and also gives better results than conventional FCM algorithm, in terms of PSNR and PRI.Key Words: Colour Image Segmentation, JND Histogram, Fuzzy C-means Clustering, Fast FCM IntroductionSegmentation involves partitioning an image into a set of homogeneous and meaningful regions, such that the pixels in each partitioned region posses an identical set of properties. Fuzzy set theory has been extensively applied in the area of image segmentation. The concepts of fuzzy first order and second order statistic like fuzzy histogram and co-occurrence matrices have been presented in [1,2]. These measures have been demonstrated to yield excellent segmentation results for the images having bimodal or multimodal histograms. The fuzzy second order statistics characterize textured images in a better way than their corresponding hard counterparts [3]. A number of research reports indicate the superiority of fuzzy sets in segmenting an image over its crisp counterparts.Fuzzy approaches to pixel classification have found applications in problems where precise knowledge about the pattern classes is not available, large number of pattern samples are not available for statistical estimation of parameters and patterns have partial membership to different classes. In many real world images, the clusters are not disjoint and a simple pattern or a pixel in an image may belong to different clusters. For example, a particular pixel in the sky may belong partly to sky and partly to the cloudy-sky class. Fuzzy approaches to supervised pattern classification and clustering may be found in [4]. Clustering methods are considered as an unsupervised classification technique, where there is no need for prior

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“…One of the most significant advantages of fuzzy K-means is that it more naturally handles situations in which subclasses are formed by mixing (or interpolating) extreme examples. Fuzzy K-means has been widely used in remote sensing, and many algorithms are derived from it [20]- [25]. However, these foregoing algorithms have similar drawbacks when used in remote-sensing imagery analysis.…”

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confidence: 99%

A Fuzzy-Statistics-Based Affinity Propagation Technique for Clustering in Multispectral Images

Yang

Bruzzone

Sun

et al. 2010

IEEE Trans. Geosci. Remote Sensing

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Abstract-Due to a high number of spectral channels and a large information quantity, multispectral remote-sensing images are difficult to be classified with high accuracy and efficiency by conventional classification methods, particularly when training data are not available and when unsupervised clustering techniques should be considered for data analysis. In this paper, we propose a novel image clustering method [called fuzzystatistics-based affinity propagation (FS-AP)] which is based on a fuzzy statistical similarity measure (FSS) to extract land-cover information in multispectral imagery. AP is a clustering algorithm proposed recently in the literature, which exhibits a fast execution speed and finds clusters with small error, particularly for large datasets. FSS can get objective estimates of how closely two pixel vectors resemble each other. The proposed method simultaneously considers all data points to be equally suitable as initial exemplars, thus reducing the dependence of the final clustering from the initialization. Results obtained on three kinds of multispectral images (Landsat-7 ETM+, Quickbird, and moderate resolution imaging spectroradiometer) by comparing the proposed technique with K-means, fuzzy K-means, and AP based on Euclidean distance (ED-AP) demonstrate the good efficiency and high accuracy of FS-AP.

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Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation

Cited by 99 publications

References 39 publications

Robust c-prototypes algorithms for color image segmentation

Robust c-prototypes algorithms for color image segmentation

Color Image Segmentation using Fast Fuzzy C-Means Algorithm

A Fuzzy-Statistics-Based Affinity Propagation Technique for Clustering in Multispectral Images

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