An Improved Method for Image Segmentation Using K-Means Clustering with Neutrosophic Logic

Qureshi, Mohammad Naved; Ahamad, Mohd Vasim

doi:10.1016/j.procs.2018.05.006

Cited by 45 publications

(26 citation statements)

References 6 publications

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“…Advantages Disadvantages superpixel [5,6] reduce redundant information; less complexity cannot locate the edges accurately watershed [7,8] simple and intuition usually result in over segmention active contour models [9,10] rigorous mathematical base; sensitive noise; high computation complexity clustering [11,12] intensive value is enough; simple the number of cluster cannot be determined automatically; spatial information is ignored;…”

Section: Methodsmentioning

confidence: 99%

“…To deal with image segmentation, many approaches and strategies had been developed. For example, turbopixel/superpixel segmentation methods [5,6], watershed segmentation methods [7,8], active contour models [9,10], clustering based methods [11,12], deep learning-based methods [13,14], thresholding methods [15,16],and so on. The advantages and disadvantages of these methods are summarized in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Jiang

Yang²,

Guo

et al. 2018

Entropy

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Spatial correlation information between pixels is considered to be very important in thresholding methods. However, it is often ignored and thus unsatisfied segmentation results maybe obtained. To overcome this shortcoming, we propose a new image segmentation approach by taking not only pixels’ spatial information but also pixels’s gray level into account. First, a non-local mean filter is imposed on the image. Then the filtered image and the original image together are adopted to build a two dimensional histogram, it is called non-local mean two dimensional histogram. Finally, a minimum relative entropy criteria is used to select the ideal thresholding vector. Since the non-local mean filter process is performed in a neighborhood of current pixel, it carries out the spatial information of current pixel. Segmentation results on several images illustrate the effectiveness of the proposed thresholding method, whose segmentation accuracy are greatly improved compared to most existing thresholding methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Jiang

Yang²,

Guo

et al. 2018

Entropy

View full text Add to dashboard Cite

show abstract

“…This system is tested on many images and can work well in training. Furthermore, [23] offers the procedure to improve the image segmentation. First, the KM clustering algorithm is applied to the input image.…”

Section: Related Workmentioning

confidence: 99%

Potential Halal Tourism Destinations with Applying K-Means Clustering

Aini¹,

Khudzaeva²

2019

IJISA

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The clustering application can be used to develop a variety of tourism potential. Currently, halal tourism is a national income that increases every year and is a favorite for Indonesia. The development of halal tourism is supported by a majority population Muslim and as a halal tourist destination in the world. The objective of this study is to investigate the number of clustering with partitioning approach i.e. K-Means (KM) with two simulation scenarios. The characteristics similarity of this method refers to 11 indicators in 2017 Global Muslim Travel Index (GMTI). The output of this study is to display the information in the form of a map and make it easier for the public to determine which halal tourism destinations are high, medium, and low potential.

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“…Image segmentation is one of the most important techniques in image processing, which partitions a given image into several unique and disjoint classes according to color, texture, edge, and other parameters [1][2][3][4][5]. In the last few decades, many segmentation methods have been proposed by researchers, such as clustering, edge detection, region growing, and thresholding [6][7][8]. Among the available methods, thresholding is extensively used due its simplicity and efficiency [9].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

et al. 2019

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An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

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An Improved Method for Image Segmentation Using K-Means Clustering with Neutrosophic Logic

Cited by 45 publications

References 6 publications

Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy

Potential Halal Tourism Destinations with Applying K-Means Clustering

Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

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