Determining the Clustering Centers by Slope Difference Distribution

Wang, Zhenzhou

doi:10.1109/access.2017.2715861

Cited by 16 publications

(7 citation statements)

References 24 publications

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“…IoU will be 0 when the target pixels of the reference image and the thresholding result image do not overlap at all; IoU will be 1 when the target pixels of the reference image and the thresholding result image are a perfect match. The proposed MDSE method is compared with global Masi entropy thresholding (MET) method [30], global adaptive Tsallis entropy thresholding (TET) method [29], local Shannon entropy thresholding (SET) method [22], iterative triclass thresholding (ITT) method [42], fuzzy entropy thresholding (FET) method [31], transition region thresholding (TRT) method [20], slope difference distribution (SDD) clustering method [4], fast and robust fuzzy c-means (FR-FCM) clustering method [5], and interactive thresholding (IT) method [43]. The IT method interactively selects a segmentation threshold, and the binary image corresponding to this threshold has the smallest ME value.…”

Section: A Test Environment Quantitative Evaluation Indicator and mentioning

confidence: 99%

“…The goal is a partition of the image into coherent regions, which is an important initial step in the analysis of image content. Numerous image segmentation algorithms have been developed in the last several decades, from the earliest methods, such as image thresholding [1], region growing and merging [2]- [3], clustering [4]- [5], watershed segmentation [6]- [7], to more complex algorithms, such as active contours [8], graph cuts [9]- [10], and deep learning-based methods [11]- [12].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Image Thresholding Based on Shannon Entropy Difference and Dynamic Synergic Entropy

et al. 2020

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An automatic thresholding method based on Shannon entropy difference and dynamic synergic entropy is proposed to select a reasonable threshold from the gray level image with a unimodal, bimodal, multimodal, or peakless gray level histogram. Firstly, a new concept called Shannon entropy difference is proposed, and the stopping condition of a multi-scale multiplication transformation is automatically controlled by maximizing Shannon entropy difference to produce edge images. Secondly, the gray level image is thresholded by the gray levels in order from smallest to largest to generate a series of binary images, followed by extracting contour images from the binary images. Then, a series of gray level histograms that can dynamically reflect gray level distributions and pixel positions are constructed using the edge images and the contour images synergically. Finally, dynamic synergic Shannon entropy is calculated from this series of gray level histograms, and the threshold corresponding to maximum dynamic synergic entropy is taken as the final segmentation threshold. The experimental results on 40 synthetic images and 50 real-world images show that, although the proposed method is not superior to 8 automatic segmentation methods in computational efficiency, it has more flexible adaptivity of selecting threshold and better segmentation accuracy. INDEX TERMS Automatic thresholding, Principle of maximum entropy, Shannon entropy difference, Dynamic synergic entropy.

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Section: A Test Environment Quantitative Evaluation Indicator and mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Image Thresholding Based on Shannon Entropy Difference and Dynamic Synergic Entropy

et al. 2020

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“…In each selected channel, the pixels are clustered by the SDD clustering method as described in [27] to generate the means

μ_{l}; l = 1, \dots, L

of different pixel classes. The image in the selected channel is then segmented as

\begin{matrix} right leftthickmathspace.5em \\ I_{normalc} (x, y) = \underset{l = 1, 2, \dots, L}{\arg \min |I (x, y) - μ_{l}|} \\ x = 1, 2, \dots, X; y = 1, 2, \dots, Y \end{matrix}

where

false(x, y false)

denotes the position of the pixel in the image channel and

X \times Y

denotes the resolution of the image.…”

Section: Proposed Approachmentioning

confidence: 99%

“…In this paper, we will fuse all the clustering results obtained from the Lab colour space, YUV colour space, Hsv colour space and RGB colour space to yield the final segmentation result. To obtain the clustering results robustly, we compared the slope difference distribution (SDD) clustering method [27], the Otsu's [28] clustering method, the expectation maximisation (EM) clustering method [29] and the k ‐means clustering method [30] qualitatively and quantitatively. Experimental results showed that the SDD clustering method is more accurate than the state‐of‐the‐art clustering methods for image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Robust segmentation of the colour image by fusing the SDD clustering results from different colour spaces

Wang

2020

IET image process

Self Cite

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“…Clustering has been intensively studied in machine learning and data mining communities [1]- [5]. It aims to find the underlying intrinsic structure of each cluster from given data.…”

Section: Introductionmentioning

confidence: 99%

K-Means Clustering With Incomplete Data

Wang

et al. 2019

IEEE Access

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Clustering has been intensively studied in machine learning and data mining communities. Although demonstrating promising performance in various applications, most of the existing clustering algorithms cannot efficiently handle clustering tasks with incomplete features which is common in practical applications. To address this issue, we propose a novel K-means based clustering algorithm which unifies the clustering and imputation into one single objective function. It makes these two processes be negotiable with each other to achieve optimality. Furthermore, we design an alternate optimization algorithm to solve the resultant optimization problem and theoretically prove its convergence. The comprehensive experimental study has been conducted on nine UCI benchmark datasets and real-world applications to evaluate the performance of the proposed algorithm, and the experimental results have clearly demonstrated the effectiveness of our algorithm which outperforms several commonly-used methods for incomplete data clustering.

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Determining the Clustering Centers by Slope Difference Distribution

Cited by 16 publications

References 24 publications

Automatic Image Thresholding Based on Shannon Entropy Difference and Dynamic Synergic Entropy

Automatic Image Thresholding Based on Shannon Entropy Difference and Dynamic Synergic Entropy

Robust segmentation of the colour image by fusing the SDD clustering results from different colour spaces

K-Means Clustering With Incomplete Data

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