A Unified Collaborative Multikernel Fuzzy Clustering for Multiview Data

Zeng, Shan; Wang, Xiuying; Cui, Hui; Zheng, Chaojie; Feng, David Dagan

doi:10.1109/tfuzz.2017.2743679

Cited by 58 publications

(15 citation statements)

References 39 publications

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“…To address the above issues, private location clustering is undertaken before location substitution. Clustering is an convenient way to form data into disjointed groups based on predefined criteria, and it is capable to solve the above challenges. The location clustering operation generates several clusters that contain location points in both A ′ and TLS.…”

Section: Methodsmentioning

confidence: 99%

A differentially private method for crowdsourcing data submission

Zhang

Xiong

Ren

et al. 2018

Concurrency and Computation

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Summary In recent years, the ubiquity of mobile devices has made spatial crowdsourcing a successful business platform for conducting spatiotemporal projects. In spatial crowdsourcing, workers contribute to a project by performing a task at a specific location. However, these platforms present serious threats to people's location privacy because sensitive information may be leaked from submitted spatiotemporal data. As a result, people may be hesitant to join spatial crowdsourcing projects, which hampers further applications of this business model. In this paper, we propose a private spatial crowdsourcing data submission algorithm, called PS‐Sub. This is a differentially private method that preserves people's location privacy and provides acceptable data utility. Rigorous privacy analyses theoretically demonstrate the privacy guarantees inherent in the proposed model. Experiments based on real‐world datasets were conducted using practical evaluation metrics. The results show that our method is able to achieve location privacy preservation efficiently, at an acceptable cost for spatial crowdsourcing applications.

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Section: Methodsmentioning

confidence: 99%

A differentially private method for crowdsourcing data submission

Zhang

Xiong

Ren

et al. 2018

Concurrency and Computation

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“…A fuzzy cpartition of describes the membership function to denote the membership of a data point with all clusters. The sum of memberships for any data point must be one [26,27].…”

Section: Contrast Limited Adaptive Histogram Equalization (Clahe)mentioning

confidence: 99%

Liver Tumor Segmentation using Superpixel based Fast Fuzzy C Means Clustering

Rela¹,

Nagaraja²,

Ramana³

2020

IJACSA

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In computer aided diagnosis of liver tumor detection, tumor segmentation from the CT image is an important step. The majority of methods are not able to give an integrated structure for finding fast and effective tumor segmentation. Hence segmentation of tumor is most difficult task in diagnosing. In this paper, CT abdominal image is segmented using Superpixel-based fast Fuzzy C Means clustering algorithm to decrease the time needed for computation and eradicate the manual interface. In this algorithm, a superpixel image with perfect contour can be obtain using a Multiscale morphological gradient reconstruction operation. Superpixel is presegmentation algorithm and is employed to obtain segmentation accuracy. FCM with modified object is used to obtain the color segmentation. This method is examined on 20 CT images gathered from liveratlas database, results shows that this approach is fast and accurate compared to most of segmentation algorithms. Statistical parameters which include accuracy, precision, sensitivity, specificity, dice, rfn and rfp are calculated for segmented image. The results shows that this algorithm gives high accuracy of 99.58% and improved rfn value of 8.34% compared with methods discussed in the literature.

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“…Image segmentation algorithms can be roughly grouped into two categories -unsupervised and supervised image segmentation. Unsupervised approaches, such as clustering [1], [2], GraphCut [3], active contour model [4], watershed transform (WT) [5], hidden Markov random field (HMRF) [6], fuzzy entropy [7], etc. are useful and popular due to their simplicity without depending on training samples and labels.…”

Section: Introductionmentioning

confidence: 99%

Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation

Lei

Jia

Zhang

et al. 2019

IEEE Trans. Fuzzy Syst.

276

156

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A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation. However, most of them are timeconsuming and unable to provide desired segmentation results for color images due to two reasons. The first one is that the incorporation of local spatial information often causes a high computational complexity due to the repeated distance computation between clustering centers and pixels within a local neighboring window. The other one is that a regular neighboring window usually breaks up the real local spatial structure of images and thus leads to a poor segmentation. In this work, we propose a superpixel-based fast FCM clustering algorithm (SFFCM) that is significantly faster and more robust than stateof-the-art clustering algorithms for color image segmentation. To obtain better local spatial neighborhoods, we firstly define a multiscale morphological gradient reconstruction (MMGR) operation to obtain a superpixel image with accurate contour. In contrast to traditional neighboring window of fixed size and shape, the superpixel image provides better adaptive and irregular local spatial neighborhoods that are helpful for improving color image segmentation. Secondly, based on the obtained superpixel image, the original color image is simplified efficiently and its histogram is computed easily by counting the number of pixels in each region of the superpixel image. Finally, we implement FCM with histogram parameter on the superpixel image to obtain the final segmentation result. Experiments performed on synthetic images and real images demonstrate that the proposed algorithm provides better segmentation results and takes less time than state-of-the-art clustering algorithms for color image segmentation.

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A Unified Collaborative Multikernel Fuzzy Clustering for Multiview Data

Cited by 58 publications

References 39 publications

A differentially private method for crowdsourcing data submission

A differentially private method for crowdsourcing data submission

Liver Tumor Segmentation using Superpixel based Fast Fuzzy C Means Clustering

Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation

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