New method for obtaining proper initial clusters to perform FCM algorithm for colour image clustering

Kandi, Saeideh Gorji; Tehran, Mohammad Amani; Rahmati, Mohammad

doi:10.1080/00405000701757545

Cited by 10 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By combining the opening and closing reconstruction operations as described in equation (11) and performing it on the gradient images, the gradient images can be reconstructed.…”

Section: Image Segmentation and Extractionmentioning

confidence: 99%

“…Over the past several years, a few studies 1,7,8 have been conducted to segment fabric image. The k-means clustering algorithm 9,10 and Fuzzy C-means clustering (FCM) algorithm 11 are two widely used algorithms to segment color regions of printed fabrics and multi-color yarn-dyed fabrics. Other algorithms such as the histogram-based algorithm 5 and selforganizing maps 12 are also reported for fabric color segmentation and extraction.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Color segmentation and extraction of yarn-dyed fabric based on a hyperspectral imaging system

Zhang

et al. 2020

Textile Research Journal

View full text Add to dashboard Cite

For multi-color yarn-dyed fabrics which are cross-woven by yarns with different colors, the different colors cannot be directly measured by a traditional spectrophotometer because it can only obtain the average color of solid-color sample in the limited aperture. In this paper, a novel method for color segmentation and extraction for multi-color yarn-woven fabrics based on a Hyperspectral Imaging System (HIS) was proposed. First, the multi-color yarn-woven fabric images were acquired with the HIS. Then a space transformation based on Fréchet distance was used to transform the pre-processed hyperspectral fabric images into gray images, and then an improved watershed algorithm was used to segment the transformed gray images into different color regions. Finally, to solve the problems of over-segmentation with the improved watershed algorithm, an improved k-means clustering algorithm was adopted to merge the over-segmented color regions. The experimental results on four multi-color yarn-woven fabrics showed that the color segmentation accuracy of the proposed method outperformed the ordinary k-means, Fuzzy C-means (FCM), and Density peak cluster (DPC) algorithms on evaluation indexes of compactness (CP) and separation (SP), and the execution efficiency was improved by at least 55%. Furthermore, the color difference between the proposed method and the spectrophotometric measurements ranged from 0.60 to 0.88 CMC (2:1) (Color Measurement Committee) units, which almost satisfied the accuracy of color measurement.

show abstract

“…By combining the opening and closing reconstruction operations as described in equation (11) and performing it on the gradient images, the gradient images can be reconstructed.…”

Section: Image Segmentation and Extractionmentioning

confidence: 99%

mentioning

confidence: 99%

Color segmentation and extraction of yarn-dyed fabric based on a hyperspectral imaging system

Zhang

et al. 2020

Textile Research Journal

View full text Add to dashboard Cite

show abstract

“…Recently, some researchers used colour clustering algorithms to separate colours from fabric images. For instance, the colour image clustering FCM algorithm was used to reduce the number of colours and separate colour patterns [6]. The Gustafson-Kessel clustering algorithm was adopted in the CIE lab colour system for colour separation from machine embroidery images [7].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Recognition of Colour and Contour from Ancient Chinese Embroidery Images

Wang

Tan

et al. 2022

Fibres &Amp; Textiles in Eastern Europe

View full text Add to dashboard Cite

Ancient Chinese embroidery is an important intangible part of the cultural heritage of mankind. Its colours and contours are a major source of oriental inspiration and design elements for designers today. This study presents an effective intelligent recognition of colour and contour based on K-means++ clustering and the Canny operator for colour and contour application of ancient Chinese embroidery images and for digital inheritance and innovation. First, digital cameras and portable scanners were used in embroidery image acquisition. Second, colour level adjustment, sharpening and smoothing were specially added to the preprocessing, because of the ancient embroidery age or colour errors caused by the shooting angle. Third, K-means++ clustering was used for colour clustering. Fourth, the Canny operator was used for contour detection. After the addition of colour level adjustment and sharpening in the preprocessing, the colours and contours could be acquired accurately and more effectively from embroidery images and be read and edited independently.

show abstract

“…K‐means and fuzzy c‐means (FCM) algorithms are commonly used for color segmentation, but both of them have a limitation that they require the knowledge of the cluster number . Alternatively, the self‐organizing map (SOM), an artificial neural network with an unsupervised learning process, has demonstrated a capability of recognizing or characterizing inputs that the network has never encountered .…”

Section: Introductionmentioning

confidence: 99%

“…11 K-means and fuzzy c-means (FCM) algorithms are commonly used for color segmentation, but both of them have a limitation that they require the knowledge of the cluster number. [12][13][14] Alternatively, the self-organizing map (SOM), an artificial neural network with an unsupervised learning process, has demonstrated a capability of recognizing or characterizing inputs that the network has never encountered. 15,16 Reasonable color segmentation results could be achieved using both of SOM's unified distance matrix (U-Matrix) and density matrix when the SOM has a large number of nodes, for example, 10 × 10 nodes.…”

Section: Introductionmentioning

confidence: 99%

Color segmentation in multicolor images using node‐growing self‐organizing map

Ouyang

Yuan

2018

Color Research & Application

View full text Add to dashboard Cite

This article presents a clustering algorithm based on node‐growing self‐organizing map (NGSOM) to classify colors on color images automatically and accurately partition the regions of different colors for color measurement. Based on the CIEDE2000 criterion, pixels in a multicolor image are grouped into a number of visually distinguishable color regions in which pixel distribution information is provided as the input of the NGSOM network for further segmentation. As an unsupervised clustering algorithm, the NGSOM randomly selects two initial nodes from the input source without a predefined network structure and grows nodes according to color differences between the first best matching unit (FBMU) and the second best matching unit (SBMU) of the current input data. Unlike a traditional SOM, the NGSOM utilizes learning rates varying with the pixel distributions of major color regions. The node‐growing procedure is terminated when all the input data are examined. Compared with some commonly used color clustering algorithms, the proposed algorithm possesses a better peak signal‐to‐noise ratio (PSNR) and higher time efficiency. The NGSOM can be used for a wide range of applications, including fabric colorfastness assessment, painting conservation, and scenic identification in aerial images.

show abstract

New method for obtaining proper initial clusters to perform FCM algorithm for colour image clustering

Cited by 10 publications

References 15 publications

Color segmentation and extraction of yarn-dyed fabric based on a hyperspectral imaging system

Color segmentation and extraction of yarn-dyed fabric based on a hyperspectral imaging system

Intelligent Recognition of Colour and Contour from Ancient Chinese Embroidery Images

Color segmentation in multicolor images using node‐growing self‐organizing map

Contact Info

Product

Resources

About