Interval-valued possibilistic fuzzy C-means clustering algorithm

Ji, Zexuan; Xia, Yong; Sun, Quansen; Cao, Guo

doi:10.1016/j.fss.2013.12.011

Cited by 73 publications

(28 citation statements)

References 21 publications

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“…Among these papers [7,16,17,[23][24][25][26]32], they were all validated using databases of real images from varied sources and in combination, used several types of validation techniques, such as a visual appreciation of the end result, recognition rate, error rate, and number of produced segments. This leads to a highly-varied collection of experimental tests performed among these papers.…”

Section: Discussionmentioning

confidence: 99%

“…In Zexuan Ji et al (2014) [7], an interval-valued possibilistic fuzzy c-means clustering algorithm was proposed which uses fuzzy memberships and possibilistic typicalities in order to model uncertainty from data, thus overcoming footprint of uncertainty selection, type-reduction and defuzzification, which tend to require complex solutions for T2 FLS. The effectiveness of this approach was verified with image segmentation datasets from brain magnetic resonances as well as natural images.…”

Section: T2 Fs In Image Segmentationmentioning

confidence: 99%

“…As an attempt to improve these methods, in recent years, researchers have focused on applying IT2 or GT2 FLS to be able to have a greater handling of uncertainty, either in the information or the parameters which are used in the algorithms, and, through experimentation, it has been shown that better results are obtained than with the original methods. Several research papers have been developed using FLS in image processing; therefore, in this review, we are including all adaptations where IT2 or GT2 FLS were used; e.g., in image segmentation, Zexuan Ji et al (2014) [7], an interval-valued possibilistic FCM clustering algorithm was proposed. In image filtering (Ba, A., and Yüksel, M.E., 2008) [8], a filter based on IT2 FLS to restore images corrupted by impulsive noise was proposed.…”

Section: Introductionmentioning

confidence: 99%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

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Section: Discussionmentioning

confidence: 99%

Section: T2 Fs In Image Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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“…(3) The land-use types are classified by FCM with different fuzzifiers, and the results are integrated using IT2 FS [6]. Due to its ability to handle the uncertainty of membership values, the IT2FCM is widely used, and many derivative methods of IT2FCM have been developed, including the interval type-2 fuzzy possibilistic C-means (IFPCM) [7], interval-valued possibilistic fuzzy C-means (IPFCM) [8], general type-2 fuzzy C-means (GT2 FCM) [9], interval type-2 fuzzy C-means clustering with spatial information (IIT2-FCM) [10], and kernel interval-valued fuzzy C-Means (KIFCM) clustering algorithms [11]. As noted by Zarinbal et al [12], in some of these methods, the type-2 fuzzy membership functions are defuzzified into type-1 fuzzy membership functions during each iteration, and the distances between a sample and cluster centers should be expressed as singleton values when used to calculate the lower and upper membership grades in a certain class; otherwise, in these cases, some information would be lost.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

Guo

Huo

2017

Remote Sensing

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Abstract:Interval type-2 fuzzy c-means (IT2FCM) clustering methods for remote-sensing data classification are based on interval type-2 fuzzy sets and can effectively handle uncertainty of membership grade. However, most of these methods neglect the spatial information when they are used in image clustering. The spatial information and spectral indices are useful in remote-sensing data classification. Thus, determining how to integrate them into IT2FCM to improve the quality and accuracy of the classification is very important. This paper proposes an enhanced IT2FCM* (EnIT2FCM*) algorithm by combining spatial information and spectral indices for remote-sensing data classification. First, the new comprehensive spatial information is defined as the combination of the local spatial distance and attribute distance or membership-grade distance. Then, a novel distance metric is proposed by combining this new spatial information and the selected spectral indices; these selected spectral indices are treated as another dataset in this distance metric. To test the effectiveness of the EnIT2FCM* algorithm, four typical validity indices along with the confusion matrix and kappa coefficient are used. The experimental results show that the spatial information definition proposed here is effective, and some spectral indices and their combinations improve the performance of the EnIT2FCM*. Thus, the selection of suitable spectral indices is crucial, and the combination of soil adjusted vegetation index (SAVI) and the Automated Water Extraction Index (AWEI sh ) is the best choice of spectral indices for this method.

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“…In general, the fuzzy C-means algorithm (FCM) [7][8][9][10][11][12] is more effective.FCM allows pixels to have relation with multiple clusters with varying degrees of membership. The conventional FCM algorithm is quite difficult to deal with patterns with different volumes.…”

Section: Introductionmentioning

confidence: 99%