FCM Image Segmentation Algorithm Based on Color Space and Spatial Information

Ding, Zegang; Sun, Jin; Zhang, Yang

doi:10.7763/ijcce.2013.v2.134

Cited by 18 publications

(4 citation statements)

References 9 publications

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“…Standard fuzzy C-means, however, can not effectively compensate for intensity inhomogeneity. Ding and Jin [2][3] used first-and second-order regularization terms to estimated bias field and produce a soft segmentation. While this method has been shown to be effective in correcting for inhomogeneity, it doesn't place any contextual constraints on the membership functions.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy C-Means Image Segmentation Algorithm Based on Chaotic Simulated Annealing

Yang

Wang

2014

AMM

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Considering the problem that the traditional fuzzy c-means (FCM) image segmentation algorithm is often caught in a specific range in local search and fails to get the globally optimal solution, this paper proposed a modified FCM algorithm based on chaotic simulated annealing (CSA). It traverse all the states without repetition within a certain range to calculate the optimal solution. Experimental results show that our method converges more quickly and accurately to the global optimal and proves a promise global optimization method of high adaptability and feasibility.

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Section: Introductionmentioning

confidence: 99%

Fuzzy C-Means Image Segmentation Algorithm Based on Chaotic Simulated Annealing

Yang

Wang

2014

AMM

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“…Color space explains how the colours are represented and specifies the components of color image accurately to learn how each colors looks in spectrum [2]. In different applications, different color models are used such as computer graphics, image processing, TV broadcasting, and computer vision.…”

Section: Color To Grayscale Conversionmentioning

confidence: 99%

“…Color space is a mathematical model to represent color information as three or four different color components [2]. Color space explains how the colors are represented and specifies the components of color space accurately to learn how each color spectrum looks like1.…”

Section: Color Modelmentioning

confidence: 99%

Grey level to RGB using YCbCr color space Technique

Reddy¹,

Raju²,

Kumar³

et al. 2016

IJCA

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Converting color images to grayscale is used for various reasons, like for reproducing on monochrome devices, subsequent processing. Each pixel in color image is described by a triple (R, G, B) of intensities like red, green, and blue. But how do you map that to a single value i.e. grayscale value. There are three methods to convert it. Average, Luminosity, Lightness. Different color models are used for different applications such as computer graphics, image processing, TV broadcasting, and computer vision. But still now there is no particular method for converting of grayscale to color image. In this paper a new approach was introduce to convert the grayscale image to color by using an YCbCr color space technique. Simulation results are presented to show how this approach is used to convert the grayscale to color image.

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“…Zhao Li et al [10] also used spatial information to improve FCM algorithm, and the authors had to use FCM clustering as step initialization, then to use spatial information to eliminate noise and final used to FCM algorithm after noise reduction based on the value of the membership function. Zhengjian Ding et al [11] improved FCM algorthm for land cover classification based on combination of spatial information and pixel values. These methods have certain limitations such as only applying on satellite image processing with high resolution, a method using multispectral satellite imagery, but the accuracy is not high or unsuitable.…”

Section: Introductionmentioning

confidence: 99%

Combining Fuzzy Probability and Fuzzy Clustering for Multispectral Satellite Imagery Classification

Mai

Hung

Long

2016

JST

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In practice, the classification algorithms and the initialization of the clusters and the initial centroid of clusters have great influence on the stability of the algorithms, dealing time and classification results. Some algorithms are used commonly in data classification, but their disadvantages are low accuracy and unstability such as k-Means algorithm, c-Means algorithm, Iso-data algorithm. This paper proposes a method of combining fuzzy probability and fuzzy clustering algorithm to overcome these disadvantages. The method consists of two steps, first to calculate the number of clusters and the centroid of clusters based fuzzy probability, then to use fuzzy clustering algorithm to land-cover classification. The results showed that, the accuracy of the land cover classification using multispectral satellite images according to the developed method significantly increases compared with various algorithms such as k-Means, Iso-data.

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FCM Image Segmentation Algorithm Based on Color Space and Spatial Information

Cited by 18 publications

References 9 publications

Fuzzy C-Means Image Segmentation Algorithm Based on Chaotic Simulated Annealing

Fuzzy C-Means Image Segmentation Algorithm Based on Chaotic Simulated Annealing

Grey level to RGB using YCbCr color space Technique

Combining Fuzzy Probability and Fuzzy Clustering for Multispectral Satellite Imagery Classification

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