A novel image segmentation method based on fast density clustering algorithm

Chen, Jinyin; Zheng, Haibin; Lin, Xiang; Wu, Yangyang; Su, Mengmeng

doi:10.1016/j.engappai.2018.04.023

Cited by 35 publications

(11 citation statements)

References 28 publications

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“…The introduced proposal is compared with iterative methods based on clustering processes. Specifically, pareto‐based interval Type‐2 fuzzy C‐means with multi‐scale just noticeable difference colour histogram (PIT2FC‐MJND) [7], fuzzy C‐means with extracting chromaticity features of colours (FCMECFC) [8], improved FCM algorithm based on the morphological reconstruction and membership filtering (FRFCM) [9], image segmentation a method based on fast density clustering algorithm (IS‐FDC) [10] and block‐matching fuzzy C‐means (BMFCM) [11]. The considered metrics were misclassification ratio (MCR), dice similarity coefficient for image segmentation, intersection‐over‐union (IOU), the runtime is not considered in this document.…”

Section: Resultsmentioning

confidence: 99%

Robust Gaussian‐base radial kernel fuzzy clustering algorithm for image segmentation

et al. 2019

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To perform the image segmentation task, in this Letter, a kernel fuzzy C-means algorithm is introduced, strengthened by a robust Gaussian radial basis function kernel based on M-estimators. It is well-known that these kernels consider the squared difference as a similarity measure, which is not robust to atypical data. In this regard, the main motivation of this contribution is to improve the atypical information tolerance of these kernels, in order to make a better clustering of pixels. Experimental tests were developed considering colour images. The robustness and effectiveness of this proposal are verified by quantitative and qualitative results.

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Section: Resultsmentioning

confidence: 99%

Robust Gaussian‐base radial kernel fuzzy clustering algorithm for image segmentation

et al. 2019

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“…Image segmentation is an active research topic and many segmentation methods had been proposed up to now. The clustering based methods [6][7][8], regression based methods [9,10], and deep learning based methods [11][12][13] are the new and sophisticated methods.…”

Section: Introductionmentioning

confidence: 99%

Image segmentation based on gray level and local relative entropy two dimensional histogram

Yang¹,

Cai

2020

PLoS ONE

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Though traditional thresholding methods are simple and efficient, they may result in poor segmentation results because only image's brightness information is taken into account in the procedure of threshold selection. Considering the contextual information between pixels can improve segmentation accuracy. To to this, a new thresholding method is proposed in this paper. The proposed method constructs a new two dimensional histogram using brightness of a pixel and local relative entropy of it's neighbor pixels. The local relative entropy (LRE) measures the brightness difference between a pixel and it's neighbor pixels. The two dimensional histogram, consisting of gray level and LRE, can reflect the contextual information between pixels to a certain extent. The optimal thresholding vector is obtained via minimizing cross entropy criteria. Experimental results show that the proposed method can achieve more accurate segmentation results than other thresholding methods.

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“…In the last few years, a great variety of methods has been proposed for image segmentation, which can be summarized as four types, including region-based method, clustering-based method, graph-based method, and thresholding-based method [4][5][6]. The criterion of the region-based method is that the entire image is divided into lots of subregions continuously, and then the subregions with similar characteristics are merged to obtain objects [7]; the clustering-based method divides the image pixels into several sub-collections based on the similarity such as K-means and hierarchical clustering algorithm [8]; in the graph-based method, the global segmentation and local information processing can be combined together based on the good correspondence between image and graph theory features [9]; thresholding-based method which employs the image histogram, and classifies the image pixels into corresponding regions by comparing with threshold values [10]. The thresholding technique has become the most popular compared with the existing methods because of its high accuracy and simple implementation.…”

Section: Introductionmentioning

confidence: 99%

Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

2019

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Multilevel thresholding is a very active research field in image segmentation, and has been successfully used in various applications. However, the computational time will increase exponentially as the number of thresholds increases, and for color images which contain more information this is even worse. To overcome the drawback while maintaining segmentation accuracy, a modified version of dragonfly algorithm (DA) with opposition-based learning (OBLDA) for color image segmentation is proposed in this paper. The opposition-based learning (OBL) strategy simultaneously considers the current solution and the opposite solution, which are symmetrical in the search space. With the introduction of OBL, the proposed algorithm has a faster convergence speed and more balanced exploration–exploitation compared with the original DA. In order to clearly demonstrate the outstanding performance of the OBLDA, the proposed method is compared with seven state-of-the-art meta-heuristic algorithms, through experiments on 10 test images. The optimal threshold values are calculated by the maximization of between-class variance and Kapur’s entropy. Meanwhile, some indicators, including peak signal to noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are used as evaluation criteria in the experiments. The promising results reveal that proposed method has the advantages of high accuracy and remarkable stability. Wilcoxon’s rank sum test and Friedman test are also performed to verify the superiority of OBLDA in a statistical way. Furthermore, various satellite images are also included for robustness testing. In conclusion, the OBLDA algorithm is a feasible and effective method for multilevel thresholding color image segmentation.

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A novel image segmentation method based on fast density clustering algorithm

Cited by 35 publications

References 28 publications

Robust Gaussian‐base radial kernel fuzzy clustering algorithm for image segmentation

Robust Gaussian‐base radial kernel fuzzy clustering algorithm for image segmentation

Image segmentation based on gray level and local relative entropy two dimensional histogram

Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

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