An intelligent skin‐color capture method based on fuzzy C‐means with applications

Hsiao, Shih Wen; Yen, Chih Huang; Lee, Chu-Hsuan

doi:10.1002/col.22151

Cited by 5 publications

(6 citation statements)

References 29 publications

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“…That is, if 5.0 ≤ d ij ≤ 12.0, the value of ρ ij needs to be relatively small; and if d ij ≥ 12.0, the value of ρ ij needs to be close to 0. Figure 3 shows the relationship between d ij and ρ ij with (10). It can be found that (10) satisfies the above requirements.…”

Section: Local Density Calculated With a Student's T Kernel Functionmentioning

confidence: 78%

“…13 Partition-based algorithms are widely used because of their low time complexity and high computing efficiency, and they are also suitable for use with large datasets. A number of partition-based algorithms have been proposed for image color clustering, 10,[16][17][18][19][20][21] including k-means and FCM. However, most of the partition-based algorithms require the number of clusters to be preconfigured manually, and the initial cluster centers for these algorithms are difficult to choose.…”

Section: Related Studymentioning

confidence: 99%

“…To solve the above problems, researchers have developed certain improved algorithms. For example, Li et al 10 adopted an automatic k-means algorithm for clustering based on the hue-intensity-saturation color space to detect targets, and that method can automatically determine the number of clusters, unlike standard k-means. Mahajan et al 16 proposed an FCM-based color clustering method named kernelized fuzzy c-means (KFCM) to obtain the segmentation image.…”

Section: Related Studymentioning

confidence: 99%

“…According to (9) and (10), when calculating ρ i , it is necessary to calculate the color difference between pixel i and the other pixels. It is known that if the color difference ΔE * ab is greater than 5 CIELAB units and less than 12 CIELAB units, the difference between the two colors is moderate.…”

Section: Local Density Calculated With a Student's T Kernel Functionmentioning

confidence: 99%

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A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Zheng

Cao

et al. 2020

Color Research & Application

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Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non‐iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k‐means, fuzzy c‐means, mean‐shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.

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Section: Local Density Calculated With a Student's T Kernel Functionmentioning

confidence: 78%

Section: Related Studymentioning

confidence: 99%

Section: Related Studymentioning

confidence: 99%

Section: Local Density Calculated With a Student's T Kernel Functionmentioning

confidence: 99%

See 2 more Smart Citations

A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Zheng

Cao

et al. 2020

Color Research & Application

View full text Add to dashboard Cite

show abstract

“…Where δ is an integer value which initialized to 116, ψ x ∈ λ , λ = W x ψ v , ψ v = ψ B , and W = 93.04 and f denotes the function (Hsiao, Yen, & Lee, ), which is defined as follows:

f ((), x) = ({, \begin{array}{l} {(x)}^{\frac{3}{2}} italicif x > δ^{3} \\ \frac{x}{3 δ^{2}} + \frac{4}{29} italicOtherwise \end{array})

…”

Section: Methodsmentioning

confidence: 99%

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

Khan

Akram

Sharif

et al. 2019

Microscopy Res & Technique

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Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK‐1, ISIC MSK‐2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features.

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A decision support system using hybrid AI based on multi-image quality model and its application in color design

Lian

Wang

et al. 2020

Future Generation Computer Systems

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An intelligent skin‐color capture method based on fuzzy C‐means with applications

Cited by 5 publications

References 29 publications

A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

A decision support system using hybrid AI based on multi-image quality model and its application in color design

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